During training, the model is trained alternately with one mini-batch of labeled data and INLINEFORM0 mini-batches of unlabeled data.
EXTRACTIVE
CVT BIBREF20 is a semi-supervised learning technique whose goal is to improve the model representation using a combination of labeled and unlabeled data. During training, the model is trained alternately with one mini-batch of labeled data and INLINEFORM0 mini-batches of unlabeled data. Labeled data are input into the model to calculate the standard supervised loss for each mini-batch and the model weights are updated regularly. Meanwhile, each mini-batch of unlabeled data is selected randomly from the pool of all unlabeled data; the model computes the loss for CVT from the mini-batch of unlabeled data. This CVT loss is used to train auxiliary prediction modules, which see restricted views of the input, to match the output of the primary prediction module, which is the full model that sees all the input. Meanwhile, the auxiliary prediction modules share the same intermediate representation with the primary prediction module. Hence, the intermediate representation of the model is improved through this process. As discussed in Section SECREF3 , CVT requires primary and auxiliary prediction modules for training with unlabeled data to improve the representation. Thus, we construct both types of prediction modules for our model. The flow of unlabeled data, which is processed to obtain a prediction by each module, is shown in Fig. . The output of each prediction module is transformed into the probability distribution of each class by the softmax function and then used to calculate INLINEFORM0 , as shown in cvtloss. DISPLAYFORM0 FLOAT SELECTED: Figure 3: Two auxiliary predictions (~pt,local and ~pt,distant) are obtained from the local and global structures in the low-level module. The primary prediction ~pt,primary is obtained from the virtual logit vector ~gt
1908.01294
Introduction </s> Aut omatic Ġsummar ization , Ġmachine Ġtranslation , Ġquestion Ġanswering , Ġand Ġsemantic Ġparsing Ġoperations Ġare Ġuseful Ġfor Ġprocessing , Ġanalyzing , Ġand Ġextracting Ġmeaningful Ġinformation Ġfrom Ġtext . ĠHowever , Ġwhen Ġapplied Ġto Ġlong Ġtexts , Ġthese Ġtasks Ġusually Ġrequire Ġsome Ġminimal Ġsynt actic Ġstructure Ġto Ġbe Ġidentified , Ġsuch Ġas Ġsentences ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ, Ġwhich Ġalways Ġend Ġwith Ġa Ġperiod Ġ( âĢ ľ . âĢ Ŀ ) Ġin ĠEnglish ĠB IB REF 3 Ġ. </s> However , Ġwritten ĠThai Ġdoes Ġnot Ġuse Ġan Ġexplicit Ġend - of - sent ence Ġmarker Ġto Ġidentify Ġsentence Ġboundaries ĠB IB REF 4 Ġ. ĠPrior Ġworks Ġhave Ġadapted Ġtraditional Ġmachine Ġlearning Ġmodels Ġto Ġpredict Ġthe Ġbeginning Ġposition Ġof Ġa Ġsentence . ĠThe Ġauthors Ġof ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġproposed Ġtraditional Ġmodels Ġto Ġdetermine Ġwhether Ġa Ġconsidered Ġspace Ġis Ġa Ġsentence Ġboundary Ġbased Ġon Ġthe Ġwords Ġand Ġtheir Ġpart Ġof Ġspeech Ġ( POS ) Ġnear Ġthe Ġspace . ĠMeanwhile , ĠZhou ĠN . Ġet Ġal . ĠB IB REF 8 Ġconsidered ĠThai Ġsentence Ġsegment ation Ġas Ġa Ġsequence Ġtagging Ġproblem Ġand Ġproposed Ġa ĠCR F - based Ġmodel Ġwith Ġn - gram Ġembed ding Ġto Ġpredict Ġwhich Ġword Ġis Ġthe Ġsentence Ġboundary . ĠThis Ġmethod Ġachieves Ġthe Ġstate - of - the - art Ġresult Ġfor ĠThai Ġsentence Ġsegment ation Ġand Ġachieves Ġgreater Ġaccuracy Ġthan Ġother Ġmodels Ġby Ġapproximately Ġ10 % Ġon Ġan ĠOr chid Ġdataset ĠB IB REF 9 Ġ. </s> Several Ġdeep Ġlearning Ġapproaches Ġhave Ġbeen Ġapplied Ġin Ġvarious Ġtasks Ġof Ġnatural Ġlanguage Ġprocessing Ġ( N LP ), Ġincluding Ġthe Ġlong Ġshort - term Ġmemory ĠB IB REF 10 Ġ, Ġself - att ention ĠB IB REF 11 Ġ, Ġand Ġother Ġmodels . ĠHuang ĠZ . Ġet Ġal . ĠB IB REF 12 Ġproposed Ġa Ġdeep Ġlearning Ġsequence Ġtagging Ġmodel Ġcalled ĠBi - L ST M - CR F , Ġwhich Ġintegrates Ġa Ġconditional Ġrandom Ġfield Ġ( CR F ) Ġmodule Ġto Ġgain Ġthe Ġbenefit Ġof Ġboth Ġdeep Ġlearning Ġand Ġtraditional Ġmachine Ġlearning Ġapproaches . ĠIn Ġtheir Ġexperiments , Ġthe ĠBi - L ST M - CR F Ġmodel Ġachieved Ġan Ġimproved Ġlevel Ġof Ġaccuracy Ġin Ġmany ĠN LP Ġsequence Ġtagging Ġtasks , Ġsuch Ġas Ġnamed Ġentity Ġrecognition , ĠPOS Ġtagging Ġand Ġchunk ing . </s> The ĠCR F Ġmodule Ġachieved Ġthe Ġbest Ġresult Ġon Ġthe ĠThai Ġsentence Ġsegment ation Ġtask ĠB IB REF 8 Ġ; Ġtherefore , Ġwe Ġadopt Ġthe ĠBi - L ST M - CR F Ġmodel Ġas Ġour Ġbaseline . ĠThis Ġpaper Ġmakes Ġthe Ġfollowing Ġthree Ġcontributions Ġto Ġimprove ĠBi - L ST M - CR F Ġfor Ġsentence Ġsegment ation . </s> First , Ġwe Ġpropose Ġadding Ġn - gram Ġembed ding Ġto ĠBi - L ST M - CR F Ġdue Ġto Ġits Ġsuccess Ġin ĠB IB REF 8 Ġand ĠB IB REF 12 Ġ. ĠBy Ġincluding Ġn - gram Ġembed ding , Ġthe Ġmodel Ġcan Ġcapitalize Ġon Ġboth Ġapproaches . ĠFirst , Ġthe Ġmodel Ġgains Ġthe Ġability Ġto Ġextract Ġpast Ġand Ġfuture Ġinput Ġfeatures Ġand Ġsentence Ġlevel Ġtag Ġinformation Ġfrom ĠBi - L ST M - CR F ; Ġmoreover , Ġwith Ġthe Ġn - gram Ġaddition , Ġit Ġcan Ġalso Ġextract Ġa Ġlocal Ġrepresentation Ġfrom Ġn - gram Ġembed ding , Ġwhich Ġhelps Ġin Ġcapturing Ġword Ġgroups Ġthat Ġexist Ġnear Ġsentence Ġboundary . ĠAlthough ĠJac ovi ĠA . Ġet Ġal . ĠB IB REF 13 Ġreported Ġthat Ġa Ġconv olution al Ġneural Ġnetwork Ġ( CNN ) Ġcan Ġbe Ġused Ġas Ġan Ġn - gram Ġdetector Ġto Ġcapture Ġlocal Ġfeatures , Ġwe Ġchose Ġn - gram Ġembed ding Ġover Ġa ĠCNN Ġdue Ġto Ġits Ġbetter Ġaccuracy , Ġas Ġwill Ġbe Ġshown Ġin ĠSection ĠSEC REF 8 Ġ. </s> Second , Ġwe Ġpropose Ġadding Ġincorpor ative Ġdistant Ġrepresentation Ġinto Ġthe Ġmodel Ġvia Ġa Ġself - att ention Ġmechanism , Ġwhich Ġcan Ġfocus Ġon Ġthe Ġkeywords Ġof Ġdependent Ġclauses Ġthat Ġare Ġfar Ġfrom Ġthe Ġconsidered Ġword . ĠSelf - att ention Ġhas Ġbeen Ġused Ġin Ġmany Ġrecent Ġstate - of - the - art Ġmodels , Ġmost Ġnotably Ġthe Ġtransformer ĠB IB REF 11 Ġand ĠB ERT ĠB IB REF 14 Ġ. ĠB ERT Ġhas Ġoutper formed ĠBi - L ST M Ġon Ġnumerous Ġtasks , Ġincluding Ġquestion Ġanswering Ġand Ġlanguage Ġinference . ĠTherefore , Ġwe Ġchoose Ġto Ġuse Ġself - att ention Ġmodules Ġto Ġextract Ġdistant Ġrepresentations Ġalong Ġwith Ġlocal Ġrepresentations Ġto Ġimprove Ġmodel Ġaccuracy . </s> Third , Ġwe Ġalso Ġapply Ġsemi - super vised Ġlearning ĠB IB REF 15 Ġ, Ġallowing Ġus Ġto Ġemploy Ġunlimited Ġamounts Ġof Ġunl abel ed Ġdata , Ġwhich Ġis Ġparticularly Ġimportant Ġfor Ġlow - resource Ġlanguages Ġsuch Ġas ĠThai , Ġfor Ġwhich Ġannotation Ġis Ġcostly Ġand Ġtime - consuming . ĠMany Ġsemi - super vised Ġlearning Ġapproaches Ġhave Ġbeen Ġproposed Ġin Ġthe Ġcomputer Ġvision ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġand Ġnatural Ġlanguage Ġprocessing ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġfields . ĠOur Ġchoice Ġfor Ġsemi - super vised Ġlearning Ġto Ġenhance Ġmodel Ġrepresentation Ġis ĠCross - View ĠTraining Ġ( CV T ) ĠB IB REF 20 Ġ. ĠClark ĠK . Ġet Ġal . ĠB IB REF 20 Ġclaims Ġthat ĠCV T Ġcan Ġimprove Ġthe Ġrepresentation Ġlayers Ġof Ġthe Ġmodel , Ġwhich Ġis Ġour Ġgoal . ĠHowever , ĠCV T Ġwas Ġnot Ġdesigned Ġto Ġbe Ġintegrated Ġwith Ġself - att ention Ġand ĠCR F Ġmodules ; Ġconsequently , Ġwe Ġprovide Ġa Ġmodified Ġversion Ġof ĠCV T Ġin Ġthis Ġwork . </s> Based Ġon Ġthe Ġabove Ġthree Ġcontributions , Ġwe Ġpursue Ġtwo Ġmain Ġexperiments . ĠThe Ġfirst Ġexperiment Ġwas Ġconducted Ġon Ġtwo ĠThai Ġdatasets , ĠOr chid Ġand ĠU G WC ĠB IB REF 21 Ġ, Ġto Ġevaluate Ġour ĠThai Ġsentence Ġsegment ation Ġmodel . ĠIn Ġthis Ġcase , Ġour Ġmodel Ġachieves ĠF 1 Ġscores Ġof Ġ92 . 5 % Ġand Ġ88 . 9 % Ġon ĠOr chid Ġand ĠU G WC , Ġrespectively , Ġand Ġit Ġoutper forms Ġall Ġthe Ġbaseline Ġmodels . ĠThe Ġsecond Ġexperiment Ġwas Ġexecuted Ġon Ġthe ĠI W SL T Ġdataset ĠB IB REF 22 Ġand Ġinvolves Ġan ĠEnglish - language Ġpunct uation Ġrestoration Ġtask . ĠThis Ġexperiment Ġdemonstrates Ġthat Ġour Ġmodel Ġis Ġgeneral izable Ġto Ġdifferent Ġlanguages . ĠOur Ġmodel , Ġwhich Ġdoes Ġnot Ġrequire Ġpret rained Ġword Ġvectors , Ġimproved Ġthe Ġoverall ĠF 1 Ġscore Ġby Ġ0 . 9 % Ġcompared Ġto Ġthe Ġbas elines , Ġincluding Ġa Ġmodel Ġthat Ġuses Ġpret rained Ġword Ġvectors . </s> There Ġare Ġfive Ġsections Ġin Ġthe Ġremainder Ġof Ġthis Ġpaper . ĠSection ĠSEC REF 2 Ġreviews Ġthe Ġrelated Ġworks Ġon ĠThai Ġsentence Ġsegment ation , ĠEnglish Ġpunct uation Ġrestoration Ġand Ġintroduces Ġthe Ġoriginal ĠCV T . ĠSection ĠSEC REF 3 Ġdescribes Ġthe Ġproposed Ġmodel Ġarchitecture Ġand Ġthe Ġintegration Ġof Ġcross - view Ġtraining . ĠThe Ġdatasets , Ġimplementation Ġprocess Ġand Ġevaluation Ġmetrics Ġare Ġexplained Ġin ĠSection ĠSEC REF 4 Ġ. ĠThe Ġresults Ġof Ġthe Ġexperiments Ġare Ġdiscussed Ġin ĠSection ĠSEC REF 5 Ġ. ĠFinally , ĠSection ĠSEC REF 6 Ġconcludes Ġthe Ġpaper . </s> Related ĠWorks </s> This Ġsection Ġincludes Ġthree Ġsubsections . ĠThe Ġfirst Ġsubsection Ġconcerns ĠThai Ġsentence Ġsegment ation , Ġwhich Ġis Ġthe Ġmain Ġfocus Ġof Ġthis Ġwork . ĠThe Ġtask Ġof ĠEnglish Ġpunct uation Ġrestoration , Ġwhich Ġis Ġsimilar Ġto Ġour Ġmain Ġtask , Ġis Ġdescribed Ġin Ġthe Ġsecond Ġsubsection . ĠThe Ġlast Ġsubsection Ġdescribes Ġthe Ġoriginal ĠCross - View ĠTraining Ġinitially Ġproposed Ġin ĠB IB REF 20 Ġ. </s> Th ai Ġsentence Ġsegment ation </s> In ĠThai , Ġtexts Ġdo Ġnot Ġcontain Ġmarkers Ġthat Ġdefinitively Ġidentify Ġsentence Ġboundaries . ĠInstead , Ġwritten ĠThai Ġusually Ġuses Ġa Ġspace Ġas Ġthe Ġvital Ġelement Ġthat Ġseparates Ġtext Ġinto Ġsentences . ĠHowever , Ġthere Ġare Ġthree Ġways Ġthat Ġspaces Ġare Ġused Ġin Ġthis Ġcontext ĠB IB REF 23 Ġ: Ġbefore Ġand Ġafter Ġan Ġinter jection , Ġbefore Ġconj unctions , Ġand Ġbefore Ġand Ġafter Ġnumeric Ġexpressions . ĠTherefore , Ġsegment ing Ġtext Ġinto Ġsentences Ġcannot Ġbe Ġperformed Ġsimply Ġby Ġsplitting Ġa Ġtext Ġat Ġthe Ġspaces . </s> Previous Ġworks Ġfrom ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġhave Ġfocused Ġon Ġdis amb ig uating Ġwhether Ġa Ġspace Ġfunctions Ġas Ġthe Ġsentence Ġboundary . ĠThese Ġworks Ġextract Ġcontextual Ġfeatures Ġfrom Ġwords Ġand ĠPOS Ġaround Ġthe Ġspace . ĠThen , Ġthe Ġobtained Ġfeatures Ġaround Ġthe Ġcorresponding Ġspace Ġare Ġinput Ġinto Ġtraditional Ġmodels Ġto Ġpredict Ġwhether Ġspace Ġis Ġa Ġsentence Ġboundary . </s> Although Ġa Ġspace Ġis Ġusually Ġconsidered Ġessential Ġas Ġa Ġsentence Ġboundary Ġmarker , Ġapproximately Ġ23 % Ġof Ġthe Ġsentences Ġend Ġwithout Ġa Ġspace Ġcharacter Ġin Ġone Ġnews Ġdomain Ġcorpus ĠB IB REF 8 Ġ. ĠHence , ĠZhou ĠN . Ġet Ġal . ĠB IB REF 8 Ġproposed Ġa Ġword Ġsequence Ġtagging ĠCR F - based Ġmodel Ġin Ġwhich Ġall Ġwords Ġcan Ġbe Ġconsidered Ġas Ġcandidates Ġfor Ġthe Ġsentence Ġboundary . ĠA Ġspace Ġis Ġconsidered Ġas Ġonly Ġone Ġpossible Ġmeans Ġof Ġforming Ġa Ġsentence Ġboundary . ĠThe ĠCR F - based Ġmodel ĠB IB REF 24 Ġ, Ġwhich Ġis Ġextracted Ġfrom Ġn - gram s Ġaround Ġthe Ġconsidered Ġword , Ġachieves Ġa ĠF 1 Ġscore Ġof Ġ91 . 9 %, Ġwhich Ġis Ġapproximately Ġ10 % Ġhigher Ġthan Ġthe ĠF 1 Ġscores Ġachieved Ġby Ġother Ġmodels ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġon Ġthe ĠOr chid Ġdataset , Ġas Ġmentioned Ġin ĠB IB REF 8 Ġ. </s> In Ġthis Ġwork , Ġwe Ġadopt Ġthe Ġconcept Ġof Ġword Ġsequence Ġtagging Ġand Ġcompare Ġit Ġwith Ġtwo Ġbas elines : Ġthe ĠCR F - based Ġmodel Ġwith Ġn - gram Ġembed ding , Ġwhich Ġis Ġcurrently Ġthe Ġstate - of - the - art Ġfor ĠThai Ġsentence Ġsegment ation , Ġand Ġthe ĠBi - L ST M - CR F Ġmodel , Ġwhich Ġis Ġcurrently Ġthe Ġdeep Ġlearning Ġstate - of - the - art Ġapproach Ġfor Ġsequence Ġtagging . </s> English Ġpunct uation Ġrestoration </s> Most Ġlanguages Ġuse Ġa Ġsymbol Ġthat Ġfunctions Ġas Ġa Ġsentence Ġboundary ; Ġhowever , Ġa Ġfew Ġdo Ġnot Ġuse Ġsentence Ġmarkers Ġincluding ĠThai , ĠLa o Ġand ĠMyanmar . ĠThus , Ġfew Ġstudies Ġhave Ġinvestigated Ġsentence Ġsegment ation Ġin Ġraw Ġtext . ĠHowever , Ġstudies Ġon Ġsentence Ġsegment ation , Ġwhich Ġis Ġsometimes Ġcalled Ġsentence Ġboundary Ġdetection , Ġare Ġstill Ġfound Ġin Ġthe Ġspeech Ġrecognition Ġfield ĠB IB REF 25 Ġ. ĠThe Ġtypical Ġinput Ġto Ġspeech Ġrecognition Ġmodel Ġis Ġsimply Ġa Ġstream Ġof Ġwords . ĠIf Ġtwo Ġsentences Ġare Ġspoken Ġback Ġto Ġback , Ġby Ġdefault , Ġa Ġrecognition Ġengine Ġwill Ġtreat Ġit Ġas Ġone Ġsentence . ĠThus , Ġsentence Ġboundary Ġdetection Ġis Ġalso Ġconsidered Ġa Ġpunct uation Ġrestoration Ġtask Ġin Ġspeech Ġrecognition Ġbecause Ġwhen Ġthe Ġmodel Ġattempts Ġto Ġrestores Ġthe Ġperiod Ġin Ġthe Ġtext , Ġthe Ġsentence Ġboundary Ġposition Ġwill Ġalso Ġbe Ġdefined . </s> P unct uation Ġrestoration Ġnot Ġonly Ġprovides Ġa Ġminimal Ġsynt actic Ġstructure Ġfor Ġnatural Ġlanguage Ġprocessing , Ġsimilar Ġto Ġsentence Ġboundary Ġdetection Ġbut Ġalso Ġdramatically Ġimproves Ġthe Ġread ability Ġof Ġtranscripts . ĠTherefore , Ġpunct uation Ġrestoration Ġhas Ġbeen Ġextensively Ġstudied . ĠMany Ġapproaches Ġhave Ġbeen Ġproposed Ġfor Ġpunct uation Ġrestoration Ġthat Ġuse Ġdifferent Ġfeatures , Ġsuch Ġas Ġaudio Ġand Ġtextual Ġfeatures . ĠMoreover , Ġpunct uation Ġrestoration Ġis Ġalso Ġconsidered Ġto Ġbe Ġa Ġdifferent Ġmachine Ġlearning Ġproblem , Ġsuch Ġas Ġword Ġsequence Ġtagging Ġand Ġmachine Ġtranslation . </s> A Ġcombination Ġof Ġaudio Ġand Ġtextual Ġfeatures Ġwere Ġutilized Ġin ĠB IB REF 26 Ġ, ĠB IB REF 27 Ġ, ĠB IB REF 28 Ġto Ġpredict Ġand Ġrestore Ġpunct uation , Ġincluding Ġpitch , Ġintensity Ġand Ġpause Ġduration , Ġbetween Ġwords . ĠWe Ġignore Ġthese Ġfeatures Ġin Ġour Ġexperiment Ġbecause Ġour Ġmain Ġtask âĢĶ Th ai Ġsentence Ġsegment ation âĢĶ Ġdoes Ġnot Ġinclude Ġaudio Ġfeatures . </s> F ocusing Ġonly Ġon Ġtextual Ġfeatures , Ġthere Ġare Ġtwo Ġmain Ġapproaches , Ġnamely , Ġword Ġsequence Ġtagging Ġand Ġmachine Ġtranslation . ĠFor Ġthe Ġmachine Ġtranslation Ġapproach , Ġpunct uation Ġis Ġtreated Ġas Ġjust Ġanother Ġtype Ġof Ġtoken Ġthat Ġneeds Ġto Ġbe Ġrecovered Ġand Ġincluded Ġin Ġthe Ġoutput . ĠThe Ġmethods Ġin ĠB IB REF 29 Ġ, ĠB IB REF 30 Ġ, ĠB IB REF 31 Ġrestore Ġpunct uation Ġby Ġtranslating Ġfrom Ġunp unct uated Ġtext Ġto Ġpunct uated Ġtext . ĠHowever , Ġour Ġmain Ġtask , Ġsentence Ġsegment ation , Ġis Ġan Ġupstream Ġtask Ġin Ġtext Ġprocessing , Ġunlike Ġpunct uation Ġrestoration , Ġwhich Ġis Ġconsidered Ġa Ġdownstream Ġtask . ĠTherefore , Ġthe Ġtask Ġneeds Ġto Ġoperate Ġrapidly ; Ġconsequently , Ġwe Ġfocus Ġonly Ġon Ġthe Ġsequence Ġtagging Ġmodel , Ġwhich Ġis Ġless Ġcomplex Ġthan Ġthe Ġmachine Ġtranslation Ġmodel . </s> In Ġaddition Ġto Ġthose Ġmachine Ġtranslation Ġtasks , Ġboth Ġtraditional Ġapproaches Ġand Ġdeep Ġlearning Ġapproaches Ġmust Ġsolve Ġa Ġword Ġsequence Ġtagging Ġproblem . ĠOf Ġthe Ġtraditional Ġapproaches , Ġcontextual Ġfeatures Ġaround Ġthe Ġconsidered Ġword Ġwere Ġused Ġto Ġpredict Ġfollowing Ġpunct uation Ġin Ġthe Ġn - gram ĠB IB REF 32 Ġand ĠCR F Ġmodel Ġapproaches ĠB IB REF 33 Ġ, ĠB IB REF 34 Ġ. ĠMeanwhile , Ġin Ġthe Ġdeep Ġlearning Ġapproaches , Ġa Ġdeep Ġconv olution al Ġneural Ġnetwork ĠB IB REF 35 Ġ, ĠT - L ST M Ġ( Text ual - L ST M ) ĠB IB REF 26 Ġand Ġa Ġbid irection al ĠL ST M Ġmodel Ġwith Ġan Ġattention Ġmechanism , Ġcalled ĠT - BR NN ĠB IB REF 36 Ġ, Ġhave Ġbeen Ġadopted Ġto Ġpredict Ġa Ġpunct uation Ġsequence Ġfrom Ġthe Ġword Ġsequence . ĠT - BR NN ĠB IB REF 36 Ġwas Ġproposed Ġto Ġsolve Ġthe Ġtask Ġas Ġa Ġword - sequence Ġtagging Ġproblem , Ġand Ġit Ġis Ġcurrently Ġthe Ġbest Ġmodel Ġthat Ġuses Ġthe Ġword Ġsequence Ġtagging Ġapproach . ĠTil k ĠO . Ġet Ġal . ĠB IB REF 36 Ġalso Ġproposed Ġa Ġvariant Ġnamed ĠT - BR NN - pre , Ġwhich Ġintegrates Ġpret rained Ġword Ġvectors Ġto Ġimprove Ġthe Ġaccuracy . </s> To Ġdemonstrate Ġthat Ġour Ġmodel Ġis Ġgeneral izable Ġto Ġother Ġlanguages , Ġwe Ġcompare Ġit Ġwith Ġother Ġpunct uation Ġrestoration Ġmodels , Ġincluding ĠT - L ST M , ĠT - BR NN , Ġand ĠT - BR NN - pre . ĠThese Ġmodels Ġadopt Ġa Ġword Ġsequence Ġtagging Ġapproach Ġand Ġdo Ġnot Ġutilize Ġany Ġpros odic Ġor Ġaudio Ġfeatures . </s> Cross - View ĠTraining </s> CV T ĠB IB REF 20 Ġis Ġa Ġsemi - super vised Ġlearning Ġtechnique Ġwhose Ġgoal Ġis Ġto Ġimprove Ġthe Ġmodel Ġrepresentation Ġusing Ġa Ġcombination Ġof Ġlabeled Ġand Ġunl abel ed Ġdata . ĠDuring Ġtraining , Ġthe Ġmodel Ġis Ġtrained Ġaltern ately Ġwith Ġone Ġmini - batch Ġof Ġlabeled Ġdata Ġand ĠIN LINE FORM 0 Ġmini - bat ches Ġof Ġunl abel ed Ġdata . </s> Label ed Ġdata Ġare Ġinput Ġinto Ġthe Ġmodel Ġto Ġcalculate Ġthe Ġstandard Ġsupervised Ġloss Ġfor Ġeach Ġmini - batch Ġand Ġthe Ġmodel Ġweights Ġare Ġupdated Ġregularly . ĠMeanwhile , Ġeach Ġmini - batch Ġof Ġunl abel ed Ġdata Ġis Ġselected Ġrandomly Ġfrom Ġthe Ġpool Ġof Ġall Ġunl abel ed Ġdata ; Ġthe Ġmodel Ġcomp utes Ġthe Ġloss Ġfor ĠCV T Ġfrom Ġthe Ġmini - batch Ġof Ġunl abel ed Ġdata . ĠThis ĠCV T Ġloss Ġis Ġused Ġto Ġtrain Ġauxiliary Ġprediction Ġmodules , Ġwhich Ġsee Ġrestricted Ġviews Ġof Ġthe Ġinput , Ġto Ġmatch Ġthe Ġoutput Ġof Ġthe Ġprimary Ġprediction Ġmodule , Ġwhich Ġis Ġthe Ġfull Ġmodel Ġthat Ġsees Ġall Ġthe Ġinput . ĠMeanwhile , Ġthe Ġauxiliary Ġprediction Ġmodules Ġshare Ġthe Ġsame Ġintermediate Ġrepresentation Ġwith Ġthe Ġprimary Ġprediction Ġmodule . ĠHence , Ġthe Ġintermediate Ġrepresentation Ġof Ġthe Ġmodel Ġis Ġimproved Ġthrough Ġthis Ġprocess . </s> Similar Ġto Ġthe Ġprevious Ġwork , Ġwe Ġalso Ġapply ĠCV T Ġto Ġa Ġsequence Ġtagging Ġtask . ĠHowever , Ġour Ġmodel Ġis Ġcomposed Ġof Ġself - att ention Ġand ĠCR F Ġmodules , Ġwhich Ġwere Ġnot Ġincluded Ġin Ġthe Ġsequence Ġtagging Ġmodel Ġin ĠB IB REF 20 Ġ. ĠThe Ġprevious ĠCV T Ġwas Ġconducted Ġon Ġan ĠL ST M Ġusing Ġthe Ġconcepts Ġof Ġforward Ġand Ġbackward Ġpaths , Ġwhich Ġare Ġnot Ġintuitive ly Ġacquired Ġby Ġthe Ġself - att ention Ġmodel . ĠMoreover , Ġthe Ġoutput Ġused Ġto Ġcalculate ĠCV T Ġloss Ġwas Ġgenerated Ġby Ġthe Ġsoft max Ġfunction , Ġwhich Ġdoes Ġnot Ġoperate Ġwith ĠCR F . ĠThus , Ġin Ġour Ġstudy , Ġboth Ġthe Ġprimary Ġand Ġauxiliary Ġprediction Ġmodules Ġneeded Ġto Ġbe Ġconstructed Ġdifferently Ġfrom Ġthe Ġoriginal Ġones . </s> arch itect ure / Sem i </s> As Ġdiscussed Ġin ĠSection ĠSEC REF 3 Ġ, ĠCV T Ġrequires Ġprimary Ġand Ġauxiliary Ġprediction Ġmodules Ġfor Ġtraining Ġwith Ġunl abel ed Ġdata Ġto Ġimprove Ġthe Ġrepresentation . ĠThus , Ġwe Ġconstruct Ġboth Ġtypes Ġof Ġprediction Ġmodules Ġfor Ġour Ġmodel . ĠThe Ġflow Ġof Ġunl abel ed Ġdata , Ġwhich Ġis Ġprocessed Ġto Ġobtain Ġa Ġprediction Ġby Ġeach Ġmodule , Ġis Ġshown Ġin ĠFig . Ġ. ĠThe Ġoutput Ġof Ġeach Ġprediction Ġmodule Ġis Ġtransformed Ġinto Ġthe Ġprobability Ġdistribution Ġof Ġeach Ġclass Ġby Ġthe Ġsoft max Ġfunction Ġand Ġthen Ġused Ġto Ġcalculate ĠIN LINE FORM 0 Ġ, Ġas Ġshown Ġin Ġc vt loss . ĠDIS PLAY FORM 0 </s> The ĠIN LINE FORM 0 Ġvalue Ġis Ġbased Ġon Ġthe ĠK ull back âĢĵ Le ib ler Ġdivergence Ġ( K L Ġdivergence ) Ġbetween Ġthe Ġprobability Ġdistribution Ġof Ġthe Ġprimary ĠIN LINE FORM 1 Ġoutput Ġand Ġthose Ġof Ġtwo Ġauxiliary Ġmodules , ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġ, Ġwhere ĠIN LINE FORM 4 Ġ. ĠThe ĠKL Ġdivergence Ġat Ġeach Ġtim est ep Ġis Ġaveraged Ġwhen Ġthe Ġtim est eps Ġare Ġdropped Ġtim est eps ĠD , Ġwhich Ġis Ġdescribed Ġin ĠSection ĠUID 25 Ġ. ĠThe Ġdetails Ġof Ġthe Ġprimary Ġand Ġauxiliary Ġprediction Ġmodules , Ġwhich Ġare Ġused Ġin Ġthe ĠIN LINE FORM 5 Ġcalculation , Ġare Ġdescribed Ġin Ġthe Ġfollowing Ġsubsections . </s> In ĠB IB REF 20 Ġ, Ġthe Ġoutput Ġof Ġthe Ġprimary Ġprediction Ġmodule Ġis Ġacquired Ġfrom Ġthe Ġlast Ġlayer Ġand Ġused Ġto Ġpredict Ġtags . ĠHowever , Ġour Ġmodel Ġuses Ġa ĠCR F Ġlayer Ġto Ġdecode Ġthe Ġtags , Ġinstead Ġof Ġthe Ġsoft max Ġfunction , Ġwhose Ġinput Ġis Ġthe Ġoutput Ġfrom Ġthe Ġlast Ġfully Ġconnected Ġlayer . ĠThus , Ġthe Ġprobability Ġdistribution Ġof Ġa Ġprimary Ġprediction Ġmodule Ġshould Ġbe Ġthe Ġmarginal Ġprobability Ġacquired Ġfrom Ġthe ĠCR F Ġlayer . ĠNevertheless , Ġthe Ġforward - back ward Ġalgorithm Ġfor Ġthe Ġmarginal Ġprobability Ġcalculation Ġis Ġtime - consuming Ġwith Ġa Ġtime Ġcomplexity Ġis ĠIN LINE FORM 0 Ġ, Ġwhere ĠIN LINE FORM 1 Ġis Ġthe Ġsequence Ġlength , Ġand ĠIN LINE FORM 2 Ġis Ġthe Ġnumber Ġof Ġtags . ĠTo Ġreduce Ġthe Ġtraining Ġtime , Ġthe Ġprobability Ġdistribution Ġof Ġthe Ġprimary Ġprediction Ġmodule ĠIN LINE FORM 3 Ġis Ġinstead Ġobtained Ġfrom Ġthe Ġoutput Ġof Ġthe ĠSoft max Ġfunction , Ġwhose Ġinput Ġis Ġa Ġvirtual Ġlog it Ġvector ĠIN LINE FORM 4 Ġ, Ġas Ġshown Ġin Ġprimary . ĠDIS PLAY FORM 0 </s> Two Ġauxiliary Ġviews Ġare Ġincluded Ġto Ġimprove Ġthe Ġmodel . ĠThe Ġfirst Ġview Ġis Ġgenerated Ġfrom Ġa Ġrecurrent Ġrepresentation Ġvector ĠIN LINE FORM 0 Ġto Ġacquire Ġthe Ġlocal Ġprobability Ġdistribution ĠIN LINE FORM 1 Ġ, Ġwhere ĠIN LINE FORM 2 Ġ. ĠThe Ġsecond Ġview Ġis Ġgenerated Ġfrom Ġthe Ġlow - level Ġdistant Ġrepresentation Ġvectors ĠIN LINE FORM 3 Ġto Ġacquire Ġthe Ġprobability Ġdistribution Ġof Ġa Ġdistant Ġstructure Ġin Ġthe Ġlow - level Ġmodule ĠIN LINE FORM 4 Ġ, Ġwhere ĠIN LINE FORM 5 Ġ. ĠBy Ġgenerating Ġthe Ġviews Ġfrom Ġthese Ġrepresentation Ġvectors Ġseparately , Ġthe Ġlocal Ġand Ġdistant Ġstructures Ġin Ġthe Ġlow - level Ġmodule Ġcan Ġimprove Ġequally . </s> Although Ġboth Ġrepresentation Ġvectors Ġare Ġused Ġseparately Ġto Ġcreate Ġauxiliary Ġviews , Ġthe Ġinput Ġof Ġeach Ġstructure Ġis Ġstill Ġnot Ġrestricted , Ġunlike ĠB IB REF 20 Ġ, Ġwhere Ġthe Ġinput Ġis Ġrestricted Ġto Ġonly Ġprevious Ġor Ġfuture Ġtokens . ĠBecause ĠB ERT , Ġwhich Ġis Ġtrained Ġby Ġthe Ġmasked Ġlanguage Ġmodel , Ġoutper forms ĠOpen AI ĠG PT , Ġwhich Ġuses Ġan Ġaut ore gressive Ġapproach Ġfor Ġtraining Ġas Ġreported Ġin ĠB IB REF 14 Ġ, Ġwe Ġadopt Ġthe Ġconcept Ġof Ġthe Ġmasked Ġlanguage Ġmodel ĠB IB REF 37 Ġto Ġobtain Ġboth Ġauxiliary Ġviews . ĠThis Ġapproach Ġallows Ġthe Ġrepresentation Ġto Ġfuse Ġthe Ġleft Ġand Ġthe Ġright Ġcontext , Ġwhich Ġresults Ġin Ġa Ġbetter Ġrepresentation . ĠBy Ġusing Ġthe Ġmasked Ġlanguage Ġmodel , Ġsome Ġtokens Ġat Ġeach Ġtim est ep Ġare Ġrandomly Ġdropped Ġand Ġden oted Ġas Ġremoved Ġtokens ĠIN LINE FORM 0 Ġ; Ġthen , Ġthe Ġremaining Ġtokens Ġare Ġused Ġto Ġobtain Ġauxiliary Ġpredictions Ġin Ġthe Ġdropped Ġtim est eps ĠIN LINE FORM 1 Ġ, Ġas Ġshown Ġin ĠFig . Ġ. ĠThe Ġdetails Ġof Ġboth Ġauxiliary Ġprediction Ġmodules Ġare Ġdescribed Ġbelow . </s> arch itect ure / Mask language </s> For Ġrecurrent Ġrepresentation Ġvectors , Ġif Ġone Ġof Ġthe Ġtokens Ġis Ġdropped , Ġthe Ġrelated Ġn - gram Ġtokens Ġthat Ġinclude Ġthe Ġdropped Ġtokens Ġwill Ġalso Ġbe Ġdropped . ĠFor Ġexample , Ġif ĠIN LINE FORM 0 Ġis Ġdropped , ĠIN LINE FORM 1 Ġand ĠIN LINE FORM 2 Ġwill Ġalso Ġbe Ġdropped Ġas Ġremoved Ġtokens Ġin Ġthe Ġcase Ġof Ġa Ġbig ram . ĠThe Ġremaining Ġn - gram Ġtokens Ġare Ġthen Ġused Ġto Ġobtain Ġthe Ġrecurrent Ġrepresentation Ġvectors Ġat Ġthe Ġdropped Ġtim est eps . ĠThen , Ġthe Ġvectors Ġare Ġprovided Ġas Ġan Ġinput Ġto Ġthe Ġsoft max Ġfunction Ġto Ġobtain Ġthe Ġprobability Ġdistribution Ġof Ġthe Ġfirst Ġauxiliary Ġprediction Ġmodule , Ġas Ġshown Ġin Ġaux local . ĠDIS PLAY FORM 0 </s> In Ġthe Ġother Ġauxiliary Ġprediction Ġmodule , Ġa Ġsequence Ġof Ġthe Ġlow - level Ġdistant Ġrepresentation Ġvectors Ġis Ġgenerated Ġand Ġsome Ġtokens Ġare Ġdropped . ĠThis Ġsequence Ġof Ġvectors Ġis Ġalso Ġinput Ġinto Ġthe ĠSoft max Ġfunction , Ġjust Ġas Ġin Ġthe Ġfirst Ġauxiliary Ġprediction Ġmodule , Ġand Ġthe Ġoutput Ġis Ġanother Ġprobability Ġdistribution , Ġwhich Ġis Ġthe Ġsecond Ġauxiliary Ġprediction , Ġas Ġshown Ġin Ġaux d istant . ĠDIS PLAY FORM 0 </s> Prop osed Ġmethod </s> In Ġthis Ġsection , Ġwe Ġdescribe Ġour Ġproposed Ġmethod Ġin Ġtwo Ġsubsections . ĠThe Ġfirst Ġsubsection Ġspecifies Ġthe Ġmodel Ġarchitecture Ġand Ġthe Ġdetails Ġof Ġeach Ġmodule . ĠOur Ġfirst Ġand Ġsecond Ġcontributions , Ġwhich Ġare Ġlocal Ġand Ġdistant Ġrepresentations , Ġare Ġmainly Ġdescribed Ġin Ġthis Ġsubsection . ĠMeanwhile , Ġthe Ġsecond Ġsubsection Ġexp ounds Ġon Ġhow Ġthe Ġmodel Ġis Ġtrained Ġwith Ġunl abel ed Ġdata Ġthrough Ġthe Ġmodified ĠCV T , Ġwhich Ġis Ġour Ġthird Ġcontribution . </s> Model Ġarchitecture </s> In Ġthis Ġwork , Ġthe Ġmodel Ġpredicts Ġthe Ġtags ĠIN LINE FORM 0 Ġfor Ġthe Ġtokens Ġin Ġa Ġword Ġsequence ĠIN LINE FORM 1 Ġwhere ĠIN LINE FORM 2 Ġis Ġthe Ġsequence Ġsize Ġand ĠIN LINE FORM 3 Ġ, ĠIN LINE FORM 4 Ġdenote Ġthe Ġtoken Ġand Ġits Ġtag Ġat Ġtim est ep ĠIN LINE FORM 5 Ġ, Ġrespectively . ĠEach Ġtoken ĠIN LINE FORM 6 Ġconsists Ġof Ġa Ġword , Ġits ĠPOS Ġand Ġits Ġtype . ĠThere Ġare Ġfive Ġdefined Ġword Ġtypes : ĠEnglish , ĠThai , Ġpunct uation , Ġdigits , Ġand Ġspaces . </s> The Ġtag Ġset ĠIN LINE FORM 0 Ġis Ġpopulated Ġbased Ġon Ġthe Ġconsidered Ġtask . ĠIn ĠThai Ġsentence Ġsegment ation , Ġthe Ġassigned Ġtags Ġare ĠIN LINE FORM 1 Ġand ĠIN LINE FORM 2 Ġ; ĠIN LINE FORM 3 Ġdenotes Ġthat Ġthe Ġcorresponding Ġword Ġis Ġa Ġsentence Ġboundary Ġconsidered Ġas Ġthe Ġbeginning Ġof Ġa Ġsentence , Ġwhile Ġand ĠIN LINE FORM 4 Ġdenotes Ġthat Ġthe Ġword Ġis Ġnot Ġa Ġsentence Ġboundary . ĠMeanwhile , Ġthere Ġare Ġfour Ġtags Ġin Ġthe Ġpunct uation Ġrestoration Ġtask . ĠWords Ġnot Ġfollowed Ġby Ġany Ġpunct uation Ġare Ġtagged Ġwith ĠIN LINE FORM 5 Ġ. ĠWords Ġthat Ġare Ġfollowed Ġby Ġa Ġperiod ĠâĢ ľ . âĢ Ŀ , Ġcomma ĠâĢ ľ , âĢ Ŀ Ġor Ġquestion Ġmark ĠâĢ ľ ? âĢ Ŀ Ġare Ġtagged Ġto ĠIN LINE FORM 6 Ġ, ĠIN LINE FORM 7 Ġ, Ġand ĠIN LINE FORM 8 Ġ, Ġrespectively . </s> arch itect ure / Main Arch itect ure </s> Our Ġmodel Ġarchitecture Ġis Ġbased Ġon ĠBi - L ST M - CR F , Ġas Ġshown Ġin ĠFig . Ġ. ĠThe Ġmodel Ġis Ġdivided Ġinto Ġthree Ġmodules . ĠThe Ġfirst , Ġlow - level Ġmodule , Ġconsists Ġof Ġtwo Ġseparate Ġstructures : Ġlocal Ġand Ġdistant Ġstructures . ĠThe Ġsecond , Ġhigh - level Ġmodule , Ġcontains Ġa Ġsequence Ġof Ġstacked Ġbid irection al ĠL ST M Ġand Ġself - att ention Ġlayers . ĠThe Ġfinal Ġmodule , Ġthe Ġprediction Ġmodule , Ġis Ġresponsible Ġfor Ġpredicting Ġthe Ġtags ĠIN LINE FORM 0 Ġ. ĠEach Ġmodule Ġis Ġdescribed Ġmore Ġcompletely Ġin Ġthe Ġnext Ġthree Ġsubsections . </s> A Ġsequence Ġof Ġword Ġtokens Ġis Ġinput Ġinto Ġthe Ġlow - level Ġmodule . ĠThe Ġinput Ġtokens Ġpass Ġthrough Ġtwo Ġstructures . ĠThe Ġfirst Ġstructure Ġgenerates Ġa Ġsequence Ġof Ġlocal Ġrepresentation Ġvectors ĠIN LINE FORM 0 Ġ, Ġand Ġthe Ġsecond Ġstructure Ġgenerates Ġlow - level Ġdistant Ġrepresentation Ġvectors ĠIN LINE FORM 1 Ġ. ĠAfter Ġobtaining Ġboth Ġsequences Ġof Ġrepresentation Ġvectors , Ġthe Ġlocal Ġrepresentation Ġvectors Ġare Ġfed Ġto Ġthe ĠBi - L ST M Ġto Ġobtain Ġthe Ġrecurrent Ġrepresentation Ġvectors ĠIN LINE FORM 2 Ġ, Ġas Ġshown Ġin Ġrecurrent rep . ĠThen , Ġthe Ġrecurrent Ġand Ġdistant Ġrepresentation Ġvectors Ġare Ġconc aten ated Ġto Ġform Ġthe Ġlow - level Ġrepresentation Ġvector ĠIN LINE FORM 3 Ġ, Ġas Ġshown Ġin Ġlow - level : ĠDIS PLAY FORM 0 ĠDIS PLAY FORM 1 </s> This Ġstructure Ġis Ġshown Ġas Ġthe Ġleft Ġsub module Ġof Ġthe Ġlow - level Ġmodule Ġin ĠFig . Ġ. ĠIt Ġextracts Ġthe Ġlocal Ġrepresentation Ġvectors ĠIN LINE FORM 0 Ġ. ĠIts Ġinput Ġtokens Ġare Ġused Ġto Ġcreate Ġn - gram Ġtokens , Ġwhich Ġare Ġun ig rams ĠIN LINE FORM 1 Ġ, Ġbig rams ĠIN LINE FORM 2 Ġ, Ġand Ġtrig rams ĠIN LINE FORM 3 Ġ. ĠEach Ġn - gram Ġtoken Ġis Ġrepresented Ġas Ġan Ġembed ding Ġvector , Ġwhich Ġis Ġclassified Ġas Ġa Ġun ig ram Ġembed ding Ġvector ĠIN LINE FORM 4 Ġ, Ġa Ġbig ram Ġembed ding Ġvector ĠIN LINE FORM 5 Ġor Ġa Ġtrig ram Ġembed ding Ġvector ĠIN LINE FORM 6 Ġ. ĠEach Ġvector ĠIN LINE FORM 7 Ġis Ġmapped Ġfrom Ġa Ġtoken Ġby Ġgram Ġembed ding ĠIN LINE FORM 8 Ġ, Ġwhich Ġis Ġa Ġconc aten ated Ġvector Ġof Ġthe Ġword Ġembed ding ĠIN LINE FORM 9 Ġ, ĠPOS Ġembed ding ĠIN LINE FORM 10 Ġand Ġtype Ġembed ding ĠIN LINE FORM 11 Ġ, Ġas Ġshown Ġin Ġeq : embed ding : ĠDIS PLAY FORM 0 </s> Each Ġn - gram Ġtoken Ġat Ġtim est ep ĠIN LINE FORM 0 Ġis Ġgenerated Ġby Ġthe Ġprevious , Ġpresent Ġand Ġnext Ġtoken Ġand Ġembedded Ġinto Ġvectors Ġas Ġshown Ġin Ġun i , bi , tri . ĠThe Ġun ig ram Ġembed ding Ġat Ġtim est ep ĠIN LINE FORM 1 Ġis Ġa Ġun ig ram Ġembed ding Ġof Ġthe Ġcurrent Ġtoken ĠIN LINE FORM 2 Ġ. ĠThe Ġbig ram Ġembed ding Ġvector Ġat Ġtim est ep ĠIN LINE FORM 3 Ġis Ġa Ġbig ram Ġembed ding Ġof Ġthe Ġprevious Ġand Ġpresent Ġtokens ĠIN LINE FORM 4 Ġ, Ġand Ġthe Ġtrig ram Ġembed ding Ġvector Ġat Ġtim est ep ĠIN LINE FORM 5 Ġis Ġa Ġtrig ram Ġembed ding Ġof Ġthe Ġprevious , Ġpresent Ġand Ġnext Ġtokens ĠIN LINE FORM 6 Ġ: ĠDIS PLAY FORM 0 ĠDIS PLAY FORM 1 </s> At Ġeach Ġtim est ep ĠIN LINE FORM 0 Ġ, Ġa Ġlocal Ġrepresentation Ġvector ĠIN LINE FORM 1 Ġis Ġcombined Ġfrom Ġthe Ġn - gram Ġembed ding Ġvectors Ġgenerated Ġfrom Ġthe Ġcontext Ġaround ĠIN LINE FORM 2 Ġ. ĠA Ġcombination Ġof Ġembed ding Ġvectors , Ġwhich Ġis Ġused Ġto Ġconstruct Ġa Ġlocal Ġrepresentation Ġvector , Ġis Ġshown Ġin Ġn - gram comb ination . ĠA Ġcombination Ġconsists Ġof Ġthe Ġun ig ram , Ġbig ram , Ġand Ġtrig ram Ġembed ding Ġvectors Ġat Ġtim est eps ĠIN LINE FORM 3 Ġ, ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġand Ġit Ġis Ġa Ġconc aten ation Ġof Ġall Ġthe Ġembed ding Ġvectors : ĠDIS PLAY FORM 0 </s> The Ġdistant Ġstructure , Ġwhich Ġis Ġa Ġself - att ention Ġmodule , Ġis Ġshown Ġin ĠFig . Ġon Ġthe Ġright Ġside Ġof Ġthe Ġlow - level Ġmodule . ĠThe Ġstructure Ġextracts Ġlow - level Ġdistant Ġrepresentation Ġvectors ĠIN LINE FORM 0 Ġfrom Ġa Ġsequence Ġof Ġun ig ram Ġembed ding Ġvectors ĠIN LINE FORM 1 Ġ, Ġas Ġshown Ġin Ġdistant att ention . ĠIn Ġthis Ġcase , Ġthe Ġself - att ention Ġmodule Ġis Ġa Ġscaled Ġdot - product Ġattention ĠB IB REF 11 Ġ, Ġwhere Ġkey , Ġquery , Ġand Ġvalue Ġvectors Ġare Ġthe Ġlinear Ġprojections Ġof Ġthe Ġun ig ram Ġembed ding Ġvectors Ġshown Ġin ĠFig . Ġ. ĠThe Ġlinear Ġtransformations Ġfor Ġkey , Ġquery , Ġand Ġvalue Ġare Ġlearned Ġseparately Ġand Ġupdated Ġin Ġthe Ġmodel Ġthrough Ġback prop ag ation . ĠThe Ġoutput Ġvector , Ġwhich Ġis Ġthe Ġscaled Ġdot - product Ġattention Ġat Ġeach Ġtim est ep , Ġis Ġconc aten ated Ġwith Ġthe Ġinput Ġvector ĠIN LINE FORM 2 Ġand Ġprojected Ġby Ġa Ġlinear Ġtransformation . ĠThat Ġprojected Ġvector Ġis Ġthe Ġoutput Ġvector Ġof Ġa Ġself - att ention Ġmodule , Ġwhich Ġis Ġa Ġlow - level Ġdistant Ġrepresentation Ġvector . </s> arch itect ure / self att ention ĠDIS PLAY FORM 0 </s> The Ġlow - level Ġrepresentation Ġvectors ĠIN LINE FORM 0 Ġare Ġused Ġas Ġthe Ġinput Ġfor Ġthis Ġmodule , Ġwhich Ġoutputs Ġthe Ġhigh - level Ġrepresentation Ġvectors ĠIN LINE FORM 1 Ġwhose Ġcalculation Ġis Ġshown Ġin Ġhigh - level . ĠThe Ġhigh - level Ġmodule , Ġas Ġshown Ġin ĠFig . Ġ, Ġis Ġcomposed Ġof Ġa Ġstacked Ġbid irection al ĠL ST M Ġand Ġa Ġself - att ention Ġmodules . ĠA Ġstacked Ġbid irection al ĠL ST M Ġcontains ĠK Ġlayers Ġof Ġbid irection al ĠL ST Ms Ġin Ġwhich Ġthe Ġoutput Ġfrom Ġthe Ġprevious Ġbid irection al ĠL ST M Ġlayer Ġis Ġthe Ġinput Ġof Ġthe Ġnext Ġbid irection al ĠL ST M Ġlayer . ĠThe Ġself - att ention Ġpart Ġof Ġthis Ġstructure Ġis Ġthe Ġsame Ġas Ġthat Ġin Ġthe Ġlow - level Ġdistant Ġstructure . ĠThe Ġself - att ention Ġmodule Ġhelps Ġto Ġgenerate Ġthe Ġhigh - level Ġdistant Ġrepresentation Ġvectors Ġthat Ġare Ġoutput Ġby Ġthe Ġhigh - level Ġmodule . ĠDIS PLAY FORM 0 </s> The Ġprediction Ġmodule Ġis Ġthe Ġlast Ġmodule . ĠIt Ġincludes Ġtwo Ġlayers : Ġa Ġfully Ġconnected Ġlayer Ġand Ġa ĠCR F Ġlayer . ĠIn Ġthe Ġfully Ġconnected Ġlayer , Ġthe Ġoutput Ġvectors Ġfrom Ġthe Ġhigh - level Ġmodule Ġare Ġprojected Ġby Ġa Ġlinear Ġtransformation Ġas Ġshown Ġin Ġvirtual . ĠThe Ġpurpose Ġof Ġthis Ġlayer Ġis Ġto Ġcreate Ġthe Ġvirtual Ġlog it Ġvectors ĠIN LINE FORM 0 Ġ, Ġwhich Ġrepresent Ġthe Ġprobability Ġdistribution Ġfor ĠCV T , Ġas Ġdiscussed Ġin ĠSection ĠSEC REF 21 Ġ. ĠTherefore , Ġthe Ġnumber Ġof Ġdimensions Ġof Ġlog its Ġequals Ġthe Ġnumber Ġof Ġpossible Ġtags Ġin Ġeach Ġtask : ĠDIS PLAY FORM 0 </s> The ĠCR F Ġlayer Ġis Ġresponsible Ġfor Ġpredicting Ġthe Ġtag ĠIN LINE FORM 0 Ġof Ġa Ġtoken Ġat Ġeach Ġtim est ep , Ġas Ġshown Ġin Ġcr f . ĠThe Ġlayer Ġreceives Ġa Ġsequence Ġof Ġvirtual Ġlog it Ġvectors Ġ( ĠIN LINE FORM 1 Ġ) Ġas Ġinput Ġand Ġthen Ġdec odes Ġthem Ġto Ġa Ġsequence Ġof Ġtags ĠIN LINE FORM 2 Ġusing Ġthe ĠV iter bi Ġalgorithm . ĠDIS PLAY FORM 0 </s> Dat as ets </s> Three Ġdatasets Ġare Ġused Ġin Ġthe Ġexperiments Ġas Ġdescribed Ġin Ġthe Ġfollowing Ġsubsections . ĠWe Ġuse Ġtwo Ġdatasets Ġfor ĠThai Ġsentence Ġsegment ation , Ġand Ġthe Ġthird Ġdataset Ġis Ġused Ġfor ĠEnglish Ġpunct uation Ġrestoration . ĠThe Ġstatistics Ġof Ġthe Ġpre process ed Ġdata Ġare Ġshown Ġin ĠTable ĠT AB REF 31 Ġ, Ġincluding Ġthe Ġnumber Ġof Ġsequences Ġand Ġthe Ġnumber Ġof Ġvocabulary Ġwords Ġin Ġeach Ġdataset . ĠWe Ġalso Ġcalculate Ġthe Ġaverage Ġnumber Ġof Ġwords Ġper Ġpassage Ġin Ġthe Ġunl abel ed Ġdata Ġthat Ġdo Ġnot Ġappear Ġin Ġthe Ġlabeled Ġdata , Ġas Ġshown Ġin ĠTable ĠT AB REF 32 Ġ. </s> This Ġdataset Ġis Ġa ĠThai Ġpart - of - speech - tag ged Ġdataset Ġcontaining Ġ10 , 864 Ġsentences . ĠIn Ġthe Ġcorpus , Ġtext Ġwas Ġseparated Ġinto Ġparagraphs , Ġsentences , Ġand Ġwords Ġhierarch ically Ġby Ġlingu ists . ĠEach Ġword Ġwas Ġalso Ġmanually Ġassigned Ġa ĠPOS Ġby Ġlingu ists . ĠThese Ġdata Ġinclude Ġno Ġunl abel ed Ġdata Ġwith Ġthe Ġsame Ġword Ġsegment ation Ġand ĠPOS Ġtag Ġset . ĠHence , Ġwe Ġdo Ġnot Ġexecute ĠCV T Ġon Ġthis Ġdataset . </s> Our Ġdata Ġpre processing Ġon Ġthe ĠOR CH ID Ġcorpus Ġwas Ġsimilar Ġto Ġthat Ġin ĠB IB REF 8 Ġ: Ġall Ġthe Ġcomments Ġare Ġremoved , Ġand Ġthe Ġdata Ġare Ġpartition ed Ġinto Ġ10 Ġparts Ġcontaining Ġequal Ġnumbers Ġof Ġsentences Ġto Ġsupport Ġ10 - fold Ġcross - valid ation . ĠEach Ġtraining Ġset Ġis Ġsplit Ġinto Ġone Ġpart Ġused Ġfor Ġvalidation Ġand Ġthe Ġrest Ġis Ġused Ġfor Ġmodel Ġtraining . ĠSub sequently , Ġall Ġthe Ġwords Ġin Ġeach Ġdataset Ġare Ġconc aten ated Ġand Ġthen Ġseparated Ġinto Ġsequences Ġwith Ġ200 Ġwords Ġper Ġinstance . ĠEach Ġsequence Ġalways Ġbegins Ġwith Ġthe Ġfirst Ġword Ġof Ġa Ġsentence . ĠIf Ġa Ġsequence Ġends Ġwith Ġan Ġunfinished Ġsentence , Ġthe Ġnext Ġsequence Ġstarts Ġwith Ġthat Ġcomplete Ġsentence . </s> This ĠThai Ġdataset Ġincludes Ġmany Ġtypes Ġof Ġlabeled Ġdata Ġuseful Ġin Ġsentence Ġsegment ation Ġtasks . ĠThe Ġraw Ġtext Ġwas Ġgenerated Ġby Ġusers Ġhaving Ġconversations Ġin Ġthe Ġfinancial Ġdomain Ġand Ġwere Ġacquired Ġmainly Ġby Ġcrawling Ġsocial Ġsites . ĠThe Ġlabeled Ġdata Ġfor Ġsentence Ġsegment ation Ġwere Ġmanually Ġannot ated Ġby Ġlingu ists Ġusing Ġthe Ġdefinitions Ġin ĠB IB REF 21 Ġ. </s> At Ġthe Ġtime Ġof Ġthis Ġstudy , Ġthe Ġdataset Ġwas Ġextended Ġfrom Ġthat Ġin ĠB IB REF 21 Ġ; Ġthe Ġdata Ġwere Ġcollected Ġfrom ĠJanuary Ġ2017 Ġto ĠDecember Ġ2017 . ĠThe Ġlabeled Ġdataset Ġincludes Ġ48 , 374 Ġpassages . ĠTo Ġsupport Ġsemi - super vised Ġlearning , Ġthe Ġfirst Ġ3 Ġmonths Ġof Ġdata Ġ( 96 , 777 Ġpassages ) Ġare Ġunl abel ed . </s> Because Ġthe Ġdata Ġstem Ġfrom Ġsocial Ġmedia , Ġsome Ġtext Ġexists Ġthat Ġcannot Ġbe Ġconsidered Ġas Ġpart Ġof Ġany Ġsentence , Ġsuch Ġas Ġproduct Ġlinks , Ġsymbols Ġunrelated Ġto Ġsentences , Ġand Ġspace Ġbetween Ġsentences . ĠThese Ġportions Ġwere Ġnot Ġoriginally Ġannot ated Ġas Ġsentences Ġby Ġthe Ġlingu ists . ĠHowever , Ġin Ġthis Ġwork , Ġwe Ġtreat Ġthese Ġportions Ġas Ġindividual Ġsentences Ġand Ġtag Ġthe Ġfirst Ġword Ġof Ġeach Ġfraction Ġas Ġthe Ġsentence Ġboundary . </s> For Ġevaluation Ġpurposes , Ġthe Ġcollection Ġof Ġpassages Ġin Ġthis Ġdataset Ġis Ġbased Ġon Ġ5 - fold Ġcross - valid ation , Ġsimilar Ġto Ġthe Ġprevious Ġwork ĠB IB REF 21 Ġ. ĠThe Ġpassages Ġare Ġtreated Ġas Ġinput Ġsequences Ġfor Ġthe Ġmodel . ĠFor Ġeach Ġpassage , Ġword Ġsegment ation Ġand ĠPOS Ġtagging Ġare Ġprocessed Ġby Ġthe Ġcustom Ġmodels Ġfrom Ġthis Ġdataset . </s> We Ġadopted Ġthis ĠEnglish - language Ġdataset Ġto Ġenable Ġcomparisons Ġwith Ġmodels Ġintended Ġfor Ġother Ġlanguages . ĠThe Ġdataset Ġis Ġcomposed Ġof ĠTED Ġtalk Ġtranscripts . ĠTo Ġcompare Ġour Ġmodel Ġwith Ġthose Ġof Ġprevious Ġworks , Ġwe Ġselected Ġthe Ġtraining Ġdataset Ġfor Ġthe Ġmachine Ġtranslation Ġtrack Ġin ĠI W SL T 2012 Ġand Ġseparated Ġit Ġinto Ġtraining Ġand Ġvalidation Ġsets Ġcontaining Ġ2 . 1 Ġmillion Ġand Ġ295 Ġthousand Ġwords , Ġrespectively . ĠThe Ġtesting Ġdataset Ġis Ġthe ĠI W SL T 2011 Ġreference Ġset , Ġwhich Ġcontains Ġ13 Ġthousand Ġwords . ĠTo Ġacquire Ġunl abel ed Ġdata Ġfor Ġsemi - super vised Ġlearning , Ġwe Ġadopted Ġthe ĠI W SL T 2016 Ġmachine Ġtranslation Ġtrack Ġtraining Ġdata ; Ġduplicate Ġtalks Ġthat Ġalso Ġappear Ġin ĠI W SL T 2012 Ġare Ġdiscarded . </s> The Ġdata Ġpre processing Ġfollows Ġthe Ġprocess Ġin ĠB IB REF 36 Ġ. ĠEach Ġsequence Ġis Ġgenerated Ġfrom Ġ200 Ġwords , Ġof Ġwhich Ġbeginning Ġis Ġalways Ġthe Ġfirst Ġword Ġin Ġa Ġsentence . ĠIf Ġa Ġsentence Ġis Ġcut Ġat Ġthe Ġend Ġof Ġa Ġsequence , Ġthat Ġsentence Ġis Ġcopied Ġin Ġfull Ġto Ġthe Ġbeginning Ġof Ġthe Ġnext Ġsequence . </s> To Ġuse Ġour Ġmodel , Ġthe ĠPOS Ġof Ġeach Ġword Ġis Ġrequired . ĠHowever , Ġthe ĠI W SL T Ġdataset Ġcontains Ġonly Ġthe Ġraw Ġtext Ġof Ġtranscripts Ġand Ġdoes Ġnot Ġinclude ĠPOS Ġtags . ĠThus , Ġwe Ġimplement ĠPOS Ġtagging Ġusing Ġa Ġspecial Ġlibrary ĠB IB REF 38 Ġto Ġpredict Ġthe ĠPOS Ġof Ġeach Ġword . </s> Im plementation ĠDetail </s> Before Ġmapping Ġeach Ġtoken Ġincluded Ġin Ġthe Ġun ig ram , Ġbig ram , Ġand Ġtrig ram Ġto Ġthe Ġembed ding Ġvector , Ġwe Ġlimit Ġthe Ġminimum Ġfrequency Ġof Ġoccurring Ġwords Ġthat Ġare Ġnot Ġmarked Ġas Ġan Ġunknown Ġtoken . ĠThere Ġare Ġ2 Ġparameters Ġset Ġfor Ġthe Ġun ig ram ĠIN LINE FORM 0 Ġand Ġthe Ġremaining ĠIN LINE FORM 1 Ġ, Ġrespectively . ĠWe Ġfound Ġthat Ġmodel Ġaccuracy Ġis Ġhighly Ġsensitive Ġto Ġthese Ġparameters . ĠTherefore , Ġwe Ġuse Ġa Ġgrid Ġsearch Ġtechnique Ġto Ġfind Ġthe Ġbest Ġvalue Ġfor Ġboth Ġparameters Ġfor Ġthe Ġmodel . </s> We Ġapply Ġtwo Ġoptim izers Ġused Ġin Ġthis Ġwork : ĠAd ag ard ĠB IB REF 39 Ġand ĠAdam ĠB IB REF 40 Ġ, Ġwhose Ġlearning Ġrates Ġare Ġset Ġto Ġ0 . 02 Ġand Ġ0 . 001 Ġfor Ġthe ĠThai Ġand ĠEnglish Ġdatasets , Ġrespectively . ĠTo Ġgeneral ize Ġthe Ġmodel , Ġwe Ġalso Ġintegrate ĠL 2 Ġregular ization Ġwith Ġan Ġalpha Ġof Ġ0 . 01 Ġto Ġthe Ġloss Ġfunction Ġfor Ġmodel Ġupdating . ĠMoreover , Ġdrop out Ġis Ġapplied Ġto Ġthe Ġlocal Ġrepresentation Ġvectors , Ġrecurrent Ġrepresentation Ġvectors , Ġbetween Ġall Ġbid irection al ĠL ST Ms Ġand Ġenclosed Ġby Ġthe Ġself - att ention Ġmechanism Ġin Ġthe Ġhigh - level Ġmodule . </s> During Ġtraining , Ġboth Ġthe Ġsupervised Ġand Ġsemi - super vised Ġmodels Ġare Ġtrained Ġuntil Ġthe Ġvalidation Ġmetrics Ġstop Ġimproving ; Ġthe Ġmetrics Ġare Ġ( 1 ) Ġsentence Ġboundary ĠF 1 Ġscore Ġand Ġ( 2 ) Ġoverall ĠF 1 Ġscore Ġfor ĠThai Ġsentence Ġsegment ation Ġand ĠEnglish Ġpunct uation Ġrestoration , Ġrespectively . </s> CV T Ġhas Ġthree Ġmain Ġparameters Ġthat Ġimpact Ġmodel Ġaccuracy . ĠThe Ġfirst Ġis Ġthe Ġdrop Ġrate Ġof Ġthe Ġmasked Ġlanguage Ġmodel , Ġwhich Ġdetermines Ġthe Ġnumber Ġof Ġtokens Ġthat Ġare Ġdropped Ġand Ġused Ġfor Ġlearning Ġauxiliary Ġprediction Ġmodules Ġas Ġdescribed Ġin ĠSection ĠSEC REF 21 Ġ. ĠThe Ġsecond Ġis Ġthe Ġnumber Ġof Ġunl abel ed Ġmini - bat ches ĠIN LINE FORM 0 Ġused Ġfor Ġtraining Ġbetween Ġsupervised Ġmini - bat ches . ĠThird , Ġrather Ġthan Ġusing Ġthe Ġsame Ġdrop out Ġrate Ġfor Ġthe Ġlocal Ġrepresentation Ġvectors , Ġa Ġnew Ġdrop out Ġrate Ġis Ġassigned . </s> The Ġdetails Ġof Ġhyper param eters Ġsuch Ġas Ġthe Ġhidden Ġsize Ġof Ġeach Ġlayer Ġand Ġdrop out Ġrate Ġare Ġgiven Ġin ĠSection ĠSEC REF 7 Ġ. </s> E val uation </s> During Ġthe Ġevaluation , Ġeach Ġtask Ġis Ġassessed Ġusing Ġdifferent Ġmetrics Ġbased Ġon Ġprevious Ġworks . ĠFor ĠThai Ġsentence Ġsegment ation , Ġthree Ġmetrics Ġare Ġused Ġin Ġthe Ġevaluation : Ġsentence Ġboundary ĠF 1 Ġscore , Ġnon - sent ence Ġboundary ĠF 1 Ġscore , Ġand Ġspace Ġcorrect ĠB IB REF 8 Ġ. ĠIn Ġthis Ġwork , Ġwe Ġmainly Ġfocus Ġon Ġthe Ġperformance Ġof Ġsentence Ġboundary Ġprediction Ġand Ġnot Ġnon - sent ence Ġboundary Ġprediction Ġor Ġspace Ġprediction . ĠTherefore , Ġwe Ġmake Ġcomparisons Ġwith Ġother Ġmodels Ġregarding Ġonly Ġtheir Ġsentence Ġboundary ĠF 1 Ġscores . ĠThe Ġequation Ġfor Ġthe Ġsentence Ġboundary ĠF 1 Ġscore Ġmetric Ġis Ġshown Ġin Ġf 1 sb . ĠIn Ġcalculating Ġthe ĠF 1 Ġscore , Ġthe Ġpositive Ġclass Ġis Ġdefined Ġas Ġthe Ġsentence Ġboundary , Ġand Ġthe Ġnegative Ġclass Ġis Ġdefined Ġas Ġthe Ġnon - sent ence Ġboundary . ĠIN LINE FORM 0 ĠIN LINE FORM 1 </s> For ĠEnglish Ġpunct uation , Ġthe Ġevaluation Ġis Ġmeasured Ġon Ġeach Ġtype Ġof Ġpunct uation Ġand Ġoverall ĠF 1 Ġscore . ĠFor Ġthe Ġpunct uation Ġrestoration Ġtask , Ġwe Ġcare Ġonly Ġabout Ġthe Ġperformance Ġof Ġthe Ġsamples Ġbelonging Ġto Ġthe Ġclasses Ġthat Ġare Ġtagged Ġto Ġwords Ġfollowed Ġby Ġpunct uation ; Ġtherefore Ġclass ĠIN LINE FORM 0 Ġ, Ġwhich Ġrepresents Ġwords Ġnot Ġimmediately Ġfollowed Ġby Ġpunct uation , Ġis Ġignored Ġin Ġthe Ġevaluation . ĠConsequently , Ġthe Ġoverall ĠF 1 Ġscore Ġdoes Ġnot Ġinclude ĠIN LINE FORM 1 Ġas Ġthe Ġpositive Ġclass Ġin Ġf 1 over all . ĠIN LINE FORM 2 ĠIN LINE FORM 3 </s> To Ġcompare Ġthe Ġperformance Ġof Ġeach Ġpunct uation Ġrestoration Ġmodel Ġin Ġa Ġmanner Ġsimilar Ġto Ġsentence Ġsegment ation , Ġthe Ġ2 - class ĠF 1 Ġscore Ġis Ġcalculated Ġto Ġmeasure Ġmodel Ġaccuracy , Ġas Ġshown Ġin Ġf 12 class . ĠThe Ġcalculation Ġof Ġthis Ġmetric Ġis Ġthe Ġsame Ġas Ġthat Ġused Ġin ĠB IB REF 35 Ġ. ĠThe Ġmetric Ġconsiders Ġonly Ġwhere Ġthe Ġpunct uation Ġposition Ġis Ġand Ġignores Ġthe Ġtype Ġof Ġrestored Ġpunct uation . ĠTherefore , Ġthis Ġmeasure Ġis Ġsimilar Ġto Ġthe Ġmetric Ġsentence Ġboundary ĠF 1 , Ġwhich Ġonly Ġconsiders Ġthe Ġposition Ġof Ġthe Ġmissing Ġpunct uation . ĠIN LINE FORM 0 ĠIN LINE FORM 1 </s> Results Ġand Ġdiscussions </s> We Ġreport Ġand Ġdiscuss Ġthe Ġresults Ġof Ġour Ġtwo Ġtasks Ġin Ġfour Ġsubsections . ĠThe Ġfirst Ġand Ġsecond Ġsubsections Ġinclude Ġthe Ġeffect Ġof Ġlocal Ġrepresentation Ġand Ġdistant Ġrepresentation , Ġrespectively . ĠThe Ġimpact Ġof ĠCV T Ġis Ġexplained Ġin Ġthe Ġthird Ġsubsection . ĠThe Ġlast Ġsubsection Ġpresents Ġa Ġcomparison Ġof Ġour Ġmodel Ġand Ġall Ġthe Ġbas elines . ĠMoreover , Ġwe Ġalso Ġconduct Ġpaired Ġt - tests Ġto Ġinvestigate Ġthe Ġsignificance Ġof Ġthe Ġimprovement Ġfrom Ġeach Ġcontribution , Ġas Ġshown Ġin ĠSection ĠSEC REF 9 Ġ. </s> Effect Ġof Ġlocal Ġrepresentation </s> To Ġfind Ġthe Ġeffect Ġof Ġlocal Ġrepresentation , Ġwe Ġcompare Ġa Ġstandard ĠBi - L ST M - CR F Ġmodel Ġusing Ġour Ġfull Ġimplementation Ġto Ġthe Ġmodel Ġthat Ġincludes Ġn - gram Ġembed ding Ġto Ġextract Ġlocal Ġrepresentation . ĠIn Ġtab : th aires ult , tab : eng result , Ġthe Ġstandard ĠBi - L ST M - CR F Ġmodel Ġis Ġrepresented Ġas ĠBi - L ST M - CR F Ġ( row Ġ( e )), Ġwhile Ġthe Ġmodels Ġwith Ġlocal Ġfeatures Ġare Ġrepresented Ġas ĠIN LINE FORM 0 Ġ( row Ġ( f )). </s> The Ġresults Ġin ĠTable ĠT AB REF 45 Ġshow Ġthat Ġusing Ġn - gram Ġto Ġobtain Ġthe Ġlocal Ġrepresentation Ġimproves Ġthe ĠF 1 Ġscore Ġof Ġthe Ġmodel Ġfrom Ġ90 . 9 % Ġ( row Ġ( e )) Ġto Ġ92 . 4 % Ġ( row Ġ( f )) Ġon Ġthe ĠOr chid Ġdataset Ġand Ġfrom Ġ87 . 6 % Ġ( row Ġ( e )) Ġto Ġ88 . 7 % Ġ( row Ġ( f )) Ġon Ġthe ĠU G WC Ġdataset . ĠThese Ġresults Ġoccur Ġbecause Ġmany Ġword Ġgroups Ġexist Ġthat Ġcan Ġbe Ġused Ġto Ġsignal Ġthe Ġbeginning Ġand Ġend Ġof Ġa Ġsentence Ġin ĠThai . ĠWord Ġgroups Ġalways Ġfound Ġnear Ġsentence Ġboundaries Ġcan Ġbe Ġcategorized Ġinto Ġ2 Ġgroups . ĠThe Ġfirst Ġgroup Ġconsists Ġof Ġfinal Ġparticles , Ġe . g ., ĠâĢ ľ th ai à¸ Ļ à¸ ° | à¸ Ħ à¸ ° ĠâĢ Ŀ Ġ( na Ġ| Ġk ha ), ĠâĢ ľ th ai à¸ Ļ à¸ ° | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( na Ġ| Ġk hr á º ¡ b ), ĠâĢ ľ th ai à¹ Ģ à¸ ¥ à¸ ¢ | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( ley Ġ| Ġk hr á º ¡ b ), ĠâĢ ľ th ai à¹ ģ à¸ ¥ à¹ ī à¸ § | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( la Ãª w Ġ| Ġk hr á º ¡ b ), Ġand Ġothers . ĠThese Ġword Ġgroups Ġare Ġusually Ġused Ġat Ġthe Ġends Ġof Ġsentences Ġto Ġindicate Ġthe Ġform ality Ġlevel . ĠFor Ġinstance , Ġthe Ġmodel Ġwith Ġlocal Ġrepresentation Ġcan Ġdetect Ġthe Ġsentence Ġboundary Ġat ĠâĢ ľ th ai à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( k hr á º ¡ b ) Ġthat Ġis Ġfollowed Ġby ĠâĢ ľ th ai à¹ ģ à¸ ¥ à¹ ī à¸ § ĠâĢ Ŀ Ġ( la Ãª w ), Ġas Ġshown Ġin ĠFig . Ġ, Ġwhile Ġthe Ġmodel Ġwithout Ġlocal Ġrepresentation Ġcannot Ġdetect Ġthe Ġword Ġas Ġa Ġsentence Ġboundary . ĠThe Ġsecond Ġgroup Ġconsists Ġof Ġconj unctions Ġthat Ġare Ġalways Ġused Ġat Ġthe Ġbeginnings Ġof Ġsentences , Ġe . g ., ĠâĢ ľ th ai à¸ Ī à¸ ² à¸ ģ | à¸ Ļ à¸ ± à¹ ī à¸ Ļ Ġ( after Ġthat ) ĠâĢ Ŀ , ĠâĢ ľ th ai à¹ Ħ à¸ ¡ à¹ Ī | à¸ ĩ à¸ ± à¹ ī à¸ Ļ Ġ( other wise ) ĠâĢ Ŀ Ġand Ġothers . ĠThe Ġmodel Ġthat Ġuses Ġn - gram Ġto Ġcapture Ġword Ġgroup Ġinformation Ġis Ġbetter Ġable Ġto Ġdetect Ġword Ġgroups Ġnear Ġsentence Ġboundaries . ĠThus , Ġthis Ġmodel Ġcan Ġidentify Ġthese Ġsentence Ġboundaries Ġeasily Ġin Ġthe ĠThai Ġlanguage . </s> result / cher ry pick ng ram </s> In Ġcontrast , Ġfor Ġthe ĠEnglish Ġdataset , Ġlocal Ġrepresentation Ġusing Ġn - gram Ġdrops Ġthe Ġoverall ĠF 1 Ġscore Ġof Ġpunct uation Ġrestoration Ġfrom Ġ64 . 4 % Ġ( row Ġ( e )) Ġto Ġ63 . 6 % Ġ( row Ġ( f )), Ġas Ġshown Ġin ĠTable ĠT AB REF 47 Ġ. ĠHowever , Ġthe Ġ2 - class ĠF 1 Ġscore Ġincreases Ġslightly Ġfrom Ġ81 . 4 % Ġ( row Ġ( e )) Ġto Ġ81 . 8 % Ġ( row Ġ( f )) Ġwhen Ġcompared Ġto Ġthe ĠBi - L ST M - CR F Ġmodel , Ġwhich Ġdoes Ġnot Ġintegrate Ġn - gram Ġembed ding . ĠCommon Ġphrases Ġsuch Ġas ĠâĢ Ŀ In Ġspite Ġof âĢ Ŀ , ĠâĢ Ŀ Even Ġthough âĢ Ŀ Ġand ĠâĢ Ŀ Due Ġto Ġthe Ġfact âĢ Ŀ Ġmight Ġprovide Ġstrong Ġcues Ġfor Ġpunct uation ; Ġhowever , Ġsuch Ġphrases Ġcan Ġbe Ġfound Ġat Ġboth Ġthe Ġbeginnings Ġand Ġin Ġthe Ġmiddle Ġof Ġsentences . ĠBecause Ġsuch Ġphrases Ġcan Ġbe Ġused Ġin Ġboth Ġpositions , Ġthey Ġmay Ġfollow Ġcomm as Ġwhen Ġthey Ġare Ġin Ġthe Ġmiddle Ġof Ġthe Ġsentence Ġor Ġperiods Ġwhen Ġthey Ġare Ġat Ġthe Ġbeginning Ġof Ġa Ġsentence . ĠHowever , Ġthey Ġstill Ġfollow Ġeither Ġa Ġperiod Ġor Ġa Ġcomma ; Ġconsequently , Ġsuch Ġphrases Ġcan Ġstill Ġhelp Ġidentify Ġwhether Ġthe Ġpunct uation Ġshould Ġbe Ġrestored , Ġwhich Ġincreases Ġthe Ġ2 - class ĠF 1 Ġscore , Ġwhich Ġconsiders Ġonly Ġthe Ġpositions Ġof Ġmissing Ġpunct uation . ĠMoreover , ĠEnglish Ġdoes Ġnot Ġuse Ġthe Ġconcept Ġof Ġa Ġfinal Ġparticle Ġusually Ġfound Ġat Ġthe Ġend Ġof Ġthe Ġsentence âĢĶ similar Ġto Ġthe ĠThai Ġword Ġgroup Ġmentioned Ġearlier âĢĶ including ĠâĢ ľ th ai à¸ Ļ à¸ ° | à¸ Ħ à¸ ° ĠâĢ Ŀ ( na Ġ| Ġk ha ), ĠâĢ ľ th ai à¸ Ļ à¸ ° | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ ( na Ġ| Ġk hr á º ¡ b ), ĠâĢ ľ th ai à¹ Ģ à¸ ¥ à¸ ¢ | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( ley | k hr á º ¡ b ), ĠâĢ ľ th ai à¹ ģ à¸ ¥ à¹ ī à¸ § | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( la Ãª w Ġ| Ġk hr á º ¡ b ) Ġand Ġothers . ĠTherefore , Ġthe Ġword Ġgroups Ġcaptured Ġby Ġn - gram Ġcan Ġonly Ġhelp Ġto Ġidentify Ġwhere Ġpunct uation Ġshould Ġbe Ġrestored Ġbut Ġthey Ġdo Ġnot Ġhelp Ġthe Ġmodel Ġdetermine Ġthe Ġtype Ġof Ġpunct uation Ġthat Ġshould Ġbe Ġrestored . </s> Effect Ġof Ġdistant Ġrepresentation </s> The Ġeffect Ġof Ġthis Ġcontribution Ġcan Ġbe Ġfound Ġby Ġcomparing Ġthe Ġmodel Ġthat Ġintegrates Ġthe Ġdistant Ġrepresentation Ġand Ġthe Ġmodel Ġthat Ġdoes Ġnot . ĠThe Ġmodel Ġwith Ġdistant Ġfeatures Ġintegrated Ġis Ġrepresented Ġas ĠIN LINE FORM 0 Ġ( row Ġ( g )) Ġin Ġboth Ġtables . ĠIn Ġthis Ġcase , Ġthe Ġdistant Ġrepresentation Ġis Ġcomposed Ġof Ġthe Ġself - att ention Ġmodules Ġin Ġboth Ġthe Ġlow - Ġand Ġhigh - level Ġmodules , Ġas Ġshown Ġin ĠFig . Ġ. </s> From Ġthe Ġcombination Ġof Ġlocal Ġand Ġdistant Ġrepresentation , Ġthe Ġresults Ġin Ġtab : th aires ult , tab : eng result Ġshow Ġthat Ġthe Ġdistant Ġfeature Ġimproves Ġthe Ġaccuracy Ġof Ġthe Ġmodel Ġon Ġall Ġdatasets Ġcompared Ġto Ġthe Ġmodel Ġwith Ġno Ġdistant Ġrepresentation . ĠThe ĠF 1 Ġscores Ġof Ġthe Ġsentence Ġsegment ation Ġmodels Ġimproved Ġslightly , Ġfrom Ġ92 . 4 % Ġand Ġ88 . 7 % Ġ( row Ġ( f )) Ġto Ġ92 . 5 % Ġand Ġ88 . 8 % Ġ( row Ġ( g )) Ġon Ġthe ĠOr chid Ġand ĠU G WC Ġdatasets , Ġrespectively . ĠFor Ġthe ĠI W SL T Ġdataset , Ġthe Ġdistant Ġfeature Ġcan Ġrecover Ġthe Ġoverall ĠF 1 Ġscore Ġof Ġpunct uation Ġrestoration , Ġwhich Ġis Ġdegraded Ġby Ġthe Ġn - gram Ġembed ding ; Ġit Ġimproves Ġfrom Ġ63 . 6 % Ġ( row Ġ( f )) Ġto Ġ64 . 5 % Ġ( row Ġ( g )). ĠThe Ġreason Ġis Ġthat Ġthe Ġself - att ention Ġmodules Ġfocus Ġselectively Ġon Ġcertain Ġparts Ġof Ġthe Ġpassage . ĠThus , Ġthe Ġmodel Ġfocuses Ġon Ġthe Ġinitial Ġwords Ġof Ġthe Ġdependent Ġclauses , Ġwhich Ġhelps Ġin Ġclass ifying Ġwhich Ġtype Ġof Ġpunct uation Ġshould Ġbe Ġrestored . ĠAn Ġexample Ġis Ġshown Ġin ĠFig . Ġ: Ġthe Ġmodel Ġwith Ġdistant Ġrepresentation Ġclass ifies Ġthe Ġpunct uation Ġafter ĠâĢ Ŀ her âĢ Ŀ Ġas Ġa ĠâĢ Ŀ COM MA âĢ Ŀ Ġbecause ĠâĢ Ŀ Before âĢ Ŀ Ġis Ġthe Ġword Ġthat Ġindicates Ġthe Ġdependent Ġclause . ĠMeanwhile , Ġthe Ġmodel Ġwithout Ġdistant Ġrepresentation Ġpredicts Ġthe Ġpunct uation Ġas Ġa ĠâĢ Ŀ PER I OD âĢ Ŀ Ġbecause Ġthere Ġis Ġno Ġself - att ention Ġmodule ; Ġtherefore , Ġit Ġdoes Ġnot Ġfocus Ġon Ġthe Ġword ĠâĢ Ŀ Before âĢ Ŀ . ĠOverall , Ġthe Ġmodel Ġthat Ġincludes Ġboth Ġlocal Ġand Ġdistant Ġrepresentation Ġcan Ġgenerally Ġbe Ġused Ġfor Ġboth Ġsentence Ġsegment ation Ġand Ġpunct uation Ġrestoration , Ġand Ġit Ġoutper forms Ġboth Ġbaseline Ġmodels . </s> result / cher ry pick d istant </s> Effect Ġof ĠCross - View ĠTraining Ġ( CV T ) </s> To Ġidentify Ġthe Ġimprovement Ġfrom ĠCV T , Ġwe Ġcompared Ġthe Ġmodels Ġthat Ġuse Ġdifferent Ġtraining Ġprocesses : Ġstandard Ġsupervised Ġtraining Ġ( ĠIN LINE FORM 0 Ġ) Ġand ĠCV T Ġ( ĠIN LINE FORM 1 Ġ). ĠThe Ġmodel Ġtrained Ġwith ĠCV T Ġimproves Ġthe Ġaccuracy Ġin Ġterms Ġof Ġthe ĠF 1 Ġscore Ġon Ġboth ĠThai Ġand ĠEnglish Ġdatasets , Ġas Ġshown Ġin Ġtab : th aires ult , tab : eng result Ġ( row Ġ( g ) Ġvs Ġrow Ġ( h )). </s> This Ġexperiment Ġwas Ġconducted Ġonly Ġon Ġthe ĠU G WC Ġdataset Ġbecause Ġno Ġunl abel ed Ġdata Ġare Ġavailable Ġin Ġthe ĠOr chid Ġdataset , Ġas Ġmentioned Ġin ĠSection ĠUID 33 Ġ. ĠThe Ġmodel Ġimproves Ġthe ĠF 1 Ġscore Ġslightly , Ġfrom Ġ88 . 8 % Ġ( row Ġ( g )) Ġto Ġ88 . 9 % Ġ( row Ġ( h )) Ġon Ġthe ĠU G WC Ġdataset . ĠThis Ġresult Ġoccurs Ġbecause Ġboth Ġthe Ġlabeled Ġand Ġunl abel ed Ġdata Ġin Ġthe ĠU G WC Ġdataset Ġare Ġdrawn Ġfrom Ġthe Ġsame Ġfinance Ġdomain . ĠThe Ġaverage Ġnumber Ġof Ġnew Ġwords Ġfound Ġin Ġa Ġnew Ġunl abel ed Ġdata Ġpassage Ġis Ġonly Ġ0 . 650 , Ġas Ġshown Ġin ĠTable ĠT AB REF 32 Ġ. ĠTherefore , Ġthere Ġis Ġlittle Ġadditional Ġinformation Ġto Ġbe Ġlearned Ġfrom Ġunl abel ed Ġdata . </s> CV T Ġalso Ġimproved Ġthe Ġmodel Ġon Ġthe ĠI W SL T Ġdataset , Ġfrom Ġan Ġoverall ĠF 1 Ġscore Ġof Ġ64 . 5 % Ġ( row Ġ( g )) Ġto Ġ65 . 3 % Ġ( row Ġ( h )) Ġand Ġfrom Ġa Ġ2 - class ĠF 1 Ġscore Ġof Ġ81 . 7 % Ġto Ġ82 . 7 %. ĠBecause Ġboth Ġthe Ġlabeled Ġand Ġunl abel ed Ġdata Ġwere Ġcollected Ġfrom ĠTED Ġtalks , Ġthe Ġnumber Ġof Ġvocabulary Ġwords Ġgrows Ġsubstantially Ġmore Ġthan Ġin Ġthe ĠU G WC Ġdataset Ġbecause Ġthe Ġtalks Ġcover Ġvarious Ġtopics . ĠIn Ġthis Ġdataset , Ġaverage Ġ1 . 225 Ġnew Ġwords Ġfound Ġin Ġeach Ġnew Ġunl abel ed Ġdata Ġpassage , Ġas Ġshown Ġin ĠTable ĠT AB REF 32 Ġ; Ġconsequently Ġthe Ġmodel Ġrepresentation Ġlearns Ġnew Ġinformation Ġfrom Ġthese Ġnew Ġwords Ġeffectively . </s> Compar ison Ġwith Ġbaseline Ġmodels </s> For Ġthe ĠThai Ġsentence Ġsegment ation Ġtask , Ġour Ġmodel Ġis Ġsuperior Ġto Ġall Ġthe Ġbas elines Ġon Ġboth ĠThai Ġsentence Ġsegment ation Ġdatasets , Ġas Ġshown Ġin ĠTable ĠT AB REF 45 Ġ. ĠOn Ġthe ĠOr chid Ġdataset , Ġthe Ġsupervised Ġmodel Ġthat Ġincludes Ġboth Ġlocal Ġand Ġdistant Ġrepresentation Ġwas Ġadopted Ġfor Ġcomparison Ġto Ġthe Ġbaseline Ġmodel . ĠOur Ġmodel Ġimproves Ġthe ĠF 1 Ġscore Ġachieved Ġby ĠCR F - ng ram , Ġwhich Ġis Ġthe Ġstate - of - the - art Ġmodel Ġfor ĠThai Ġsentence Ġsegment ation Ġin ĠOr chid , Ġfrom Ġ91 . 9 % Ġ( row Ġ( d )) Ġto Ġ92 . 5 % Ġ( row Ġ( g )). ĠMeanwhile , Ġin Ġthe ĠU G WC Ġdataset , Ġour ĠCV T Ġmodel Ġ( row Ġ( h )) Ġachieves Ġan ĠF 1 Ġscore Ġof Ġ88 . 9 %, Ġwhich Ġis Ġhigher Ġthan Ġthe ĠF 1 Ġscore Ġof Ġboth Ġthe Ġbas elines Ġ( CR F - ng ram Ġand ĠBi - L ST M - CR F Ġ( rows Ġd Ġand Ġe , Ġrespectively )). ĠThus , Ġour Ġmodel Ġis Ġnow Ġthe Ġstate - of - the - art Ġmodel Ġfor ĠThai Ġsentence Ġsegment ation Ġon Ġboth Ġthe ĠOr chid Ġand ĠU G WC Ġdatasets . </s> Our Ġmodel Ġoutper forms Ġall Ġthe Ġsequence Ġtagging Ġmodels . ĠT - BR NN - pre Ġ( row Ġ( c )) Ġis Ġthe Ġcurrent Ġstate - of - the - art Ġmodel , Ġas Ġshown Ġin ĠTable ĠT AB REF 47 Ġ. ĠThe ĠCV T Ġmodel Ġimproves Ġthe Ġoverall ĠF 1 Ġscore Ġfrom Ġthe Ġ64 . 4 % Ġof ĠT - BR NN - pre Ġto Ġ65 . 3 % Ġ( row Ġ( h )), Ġdespite Ġthe Ġfact Ġthat ĠT - BR NN - pre Ġintegrates Ġa Ġpret rained Ġword Ġvector . ĠMoreover , Ġour Ġmodel Ġalso Ġachieves Ġa Ġ2 - class ĠF 1 Ġscore Ġ1 . 3 % Ġhigher Ġthan Ġthat Ġof ĠBi - L ST M - CR F Ġ( row Ġ( e )). </s> Con clusions </s> In Ġthis Ġpaper , Ġwe Ġpropose Ġa Ġnovel Ġdeep Ġlearning Ġmodel Ġfor ĠThai Ġsentence Ġsegment ation . ĠThis Ġstudy Ġmakes Ġthree Ġmain Ġcontributions . ĠThe Ġfirst Ġcontribution Ġis Ġto Ġintegrate Ġa Ġlocal Ġrepresentation Ġbased Ġon Ġn - gram Ġembed ding Ġinto Ġour Ġdeep Ġmodel . ĠThis Ġapproach Ġhelps Ġto Ġcapture Ġword Ġgroups Ġnear Ġsentence Ġboundaries , Ġallowing Ġthe Ġmodel Ġto Ġidentify Ġboundaries Ġmore Ġaccurately . ĠSecond , Ġwe Ġintegrate Ġa Ġdistant Ġrepresentation Ġobtained Ġfrom Ġself - att ention Ġmodules Ġto Ġcapture Ġsentence Ġcontextual Ġinformation . ĠThis Ġapproach Ġallows Ġthe Ġmodel Ġto Ġfocus Ġon Ġthe Ġinitial Ġwords Ġof Ġdependent Ġclauses Ġ( i . e ., ĠâĢ Ŀ Before âĢ Ŀ , ĠâĢ Ŀ If âĢ Ŀ , Ġand ĠâĢ Ŀ Although âĢ Ŀ ). ĠThe Ġlast Ġcontribution Ġis Ġan Ġadaptation Ġof ĠCV T , Ġwhich Ġallows Ġthe Ġmodel Ġto Ġutilize Ġunl abel ed Ġdata Ġto Ġproduce Ġeffective Ġlocal Ġand Ġdistant Ġrepresentations . </s> The Ġexperiment Ġwas Ġconducted Ġon Ġtwo ĠThai Ġdatasets , ĠOr chid Ġand ĠU G WC , Ġand Ġone ĠEnglish Ġpunct uation Ġrestoration Ġdataset , ĠI W SL T . ĠEnglish Ġpunct uation Ġrestoration Ġis Ġsimilar Ġto Ġour ĠThai Ġsentence Ġsegment ation . ĠOn Ġthe ĠThai Ġsentence Ġsegment ation Ġtask , Ġour Ġmodel Ġachieves ĠF 1 Ġscores Ġof Ġ92 . 5 % Ġand Ġ88 . 9 % Ġon Ġthe ĠOr chid Ġand ĠU G WC Ġdatasets , Ġconstit uting Ġa Ġrelative Ġerror Ġreduction Ġof Ġ7 . 4 % Ġand Ġ10 . 5 %, Ġrespectively . ĠOn Ġthe ĠEnglish Ġpunct uation Ġtask , Ġthe Ġ2 - class ĠF 1 Ġscore Ġreached Ġ82 . 7 % Ġwhen Ġconsidering Ġonly Ġtwo Ġpunct uation Ġclasses Ġ( making Ġthe Ġtask Ġsimilar Ġto Ġsentence Ġsegment ation Ġin ĠThai ). ĠMoreover , Ġour Ġmodel Ġoutper forms Ġthe Ġmodel Ġintegrated Ġwith Ġpret rained Ġword Ġvectors Ġin Ġterms Ġof Ġthe Ġoverall ĠF 1 Ġscore Ġon Ġthe ĠI W SL T Ġdataset . ĠBased Ġon Ġour Ġcontributions , Ġthe Ġlocal Ġrepresentation Ġscheme Ġhas Ġthe Ġhighest Ġimpact Ġon Ġthe ĠThai Ġcorpus , Ġwhile Ġthe Ġdistant Ġrepresentation Ġand ĠCV T Ġresult Ġin Ġstrong Ġimprovements Ġon Ġthe ĠEnglish Ġdataset . </s> Moreover , Ġour Ġmodel Ġcan Ġalso Ġbe Ġapplied Ġto Ġelementary Ġdiscourse Ġunit Ġ( ED U ) Ġsegment ation , Ġwhich Ġis Ġused Ġas Ġthe Ġminimal Ġsynt actic Ġunit Ġfor Ġdownstream Ġtasks Ġsuch Ġas Ġtext Ġsummar ization Ġand Ġmachine Ġtranslation . ĠHowever , Ġno Ġexperiments Ġhave Ġbeen Ġconducted Ġto Ġdetermine Ġhow Ġdifferent Ġsentences Ġand ĠED Us Ġaffect Ġdownstream Ġtasks . ĠTherefore , Ġthe Ġevaluation Ġof Ġdownstream Ġtasks Ġfrom Ġdifferent Ġsources Ġneeds Ġto Ġbe Ġstudied . </s> Acknowled gment </s> This Ġpaper Ġwas Ġsupported Ġby ĠKL abs Ġat ĠKas ik orn ĠBusiness ĠTechnology Ġ( K BT G ), Ġwho Ġprovided Ġfacilities Ġand Ġdata . ĠThe Ġprocedures Ġthat Ġwere Ġconducted Ġbased Ġon Ġsocial Ġdata Ġare Ġvisible Ġto Ġthe Ġpublic , Ġand Ġethical Ġissues Ġthat Ġcan Ġarise Ġfrom Ġthe Ġuse Ġof Ġsuch Ġdata Ġwere Ġaddressed . ĠWe Ġwould Ġlike Ġto Ġthank Ġthe Ġlingu ists ĠSas iw imon ĠKal uns ima , ĠN utch a ĠTir as aro j , ĠTant ong ĠCh ampa ib oon Ġand ĠSup aw at ĠTa er ung ru ang Ġfor Ġannot ating Ġthe ĠU G WC Ġdataset Ġused Ġin Ġthis Ġstudy . </s> Hyper param eters </s> The Ġhyper param eter Ġvalues Ġwere Ġdetermined Ġthrough Ġa Ġgrid Ġsearch Ġto Ġfind Ġtheir Ġoptimal Ġvalues Ġon Ġthe Ġdifferent Ġdatasets . ĠAll Ġthe Ġhyper param eters Ġfor Ġeach Ġdataset Ġare Ġshown Ġin ĠTable ĠT AB REF 55 Ġ. ĠThe Ġoptimal Ġvalues Ġfrom Ġthe Ġgrid Ġsearch Ġdepend Ġon Ġthe Ġtask . ĠFor ĠThai Ġsentence Ġsegment ation , Ġthe Ġhyper param eters Ġare Ġtuned Ġto Ġobtain Ġthe Ġhighest Ġsentence Ġboundary ĠF 1 Ġscore , Ġwhile Ġthe Ġoverall ĠF 1 Ġscore Ġis Ġused Ġto Ġtune Ġthe Ġparameters Ġfor ĠEnglish Ġpunct uation Ġrestoration . </s> Compar ison Ġof ĠCNN Ġand Ġn - gram Ġmodels Ġfor Ġlocal Ġrepresentation </s> Jac ovi ĠA . Ġet Ġal . ĠB IB REF 13 Ġproposed Ġthat Ġa ĠCNN Ġcan Ġbe Ġused Ġas Ġan Ġn - gram Ġdetector Ġto Ġcapture Ġlocal Ġtext Ġfeatures . ĠTherefore , Ġwe Ġalso Ġperformed Ġan Ġexperiment Ġto Ġcompare Ġa ĠCNN Ġand Ġn - gram Ġembedded Ġas Ġlocal Ġstructures . ĠThe Ġresults Ġin ĠTable ĠT AB REF 56 Ġshow Ġthat Ġthe Ġmodel Ġusing Ġthe Ġembedded Ġn - gram Ġyields Ġgreater Ġimprovement Ġthan Ġthe Ġone Ġusing Ġan Ġembedded ĠCNN Ġon Ġthe ĠOr chid Ġand ĠU G WC Ġdatasets . </s> Stat istical ĠTests Ġfor ĠThai Ġsentence Ġsegment ation </s> To Ġprove Ġthe Ġsignificance Ġof Ġthe Ġmodel Ġimprovements , Ġwe Ġcompared Ġthe Ġcross - valid ation Ġresults Ġusing Ġpaired Ġt - tests Ġto Ġobtain Ġthe Ġp - values , Ġwhich Ġare Ġshown Ġin ĠTable ĠT AB REF 57 Ġfor Ġthe ĠOr chid Ġdataset Ġand ĠTable ĠT AB REF 58 Ġfor Ġthe ĠU G WC Ġdataset .
Introduction Automatic summarization, machine translation, question answering, and semantic parsing operations are useful for processing, analyzing, and extracting meaningful information from text. However, when applied to long texts, these tasks usually require some minimal syntactic structure to be identified, such as sentences BIBREF0 , BIBREF1 , BIBREF2 , which always end with a period (“.”) in English BIBREF3 . However, written Thai does not use an explicit end-of-sentence marker to identify sentence boundaries BIBREF4 . Prior works have adapted traditional machine learning models to predict the beginning position of a sentence. The authors of BIBREF5 , BIBREF6 , BIBREF7 proposed traditional models to determine whether a considered space is a sentence boundary based on the words and their part of speech (POS) near the space. Meanwhile, Zhou N. et al. BIBREF8 considered Thai sentence segmentation as a sequence tagging problem and proposed a CRF-based model with n-gram embedding to predict which word is the sentence boundary. This method achieves the state-of-the-art result for Thai sentence segmentation and achieves greater accuracy than other models by approximately 10% on an Orchid dataset BIBREF9 . Several deep learning approaches have been applied in various tasks of natural language processing (NLP), including the long short-term memory BIBREF10 , self-attention BIBREF11 , and other models. Huang Z. et al. BIBREF12 proposed a deep learning sequence tagging model called Bi-LSTM-CRF, which integrates a conditional random field (CRF) module to gain the benefit of both deep learning and traditional machine learning approaches. In their experiments, the Bi-LSTM-CRF model achieved an improved level of accuracy in many NLP sequence tagging tasks, such as named entity recognition, POS tagging and chunking. The CRF module achieved the best result on the Thai sentence segmentation task BIBREF8 ; therefore, we adopt the Bi-LSTM-CRF model as our baseline. This paper makes the following three contributions to improve Bi-LSTM-CRF for sentence segmentation. First, we propose adding n-gram embedding to Bi-LSTM-CRF due to its success in BIBREF8 and BIBREF12 . By including n-gram embedding, the model can capitalize on both approaches. First, the model gains the ability to extract past and future input features and sentence level tag information from Bi-LSTM-CRF; moreover, with the n-gram addition, it can also extract a local representation from n-gram embedding, which helps in capturing word groups that exist near sentence boundary. Although Jacovi A. et al. BIBREF13 reported that a convolutional neural network (CNN) can be used as an n-gram detector to capture local features, we chose n-gram embedding over a CNN due to its better accuracy, as will be shown in Section SECREF8 . Second, we propose adding incorporative distant representation into the model via a self-attention mechanism, which can focus on the keywords of dependent clauses that are far from the considered word. Self-attention has been used in many recent state-of-the-art models, most notably the transformer BIBREF11 and BERT BIBREF14 . BERT has outperformed Bi-LSTM on numerous tasks, including question answering and language inference. Therefore, we choose to use self-attention modules to extract distant representations along with local representations to improve model accuracy. Third, we also apply semi-supervised learning BIBREF15 , allowing us to employ unlimited amounts of unlabeled data, which is particularly important for low-resource languages such as Thai, for which annotation is costly and time-consuming. Many semi-supervised learning approaches have been proposed in the computer vision BIBREF16 , BIBREF17 and natural language processing BIBREF18 , BIBREF19 , BIBREF20 fields. Our choice for semi-supervised learning to enhance model representation is Cross-View Training (CVT) BIBREF20 . Clark K. et al. BIBREF20 claims that CVT can improve the representation layers of the model, which is our goal. However, CVT was not designed to be integrated with self-attention and CRF modules; consequently, we provide a modified version of CVT in this work. Based on the above three contributions, we pursue two main experiments. The first experiment was conducted on two Thai datasets, Orchid and UGWC BIBREF21 , to evaluate our Thai sentence segmentation model. In this case, our model achieves F1 scores of 92.5% and 88.9% on Orchid and UGWC, respectively, and it outperforms all the baseline models. The second experiment was executed on the IWSLT dataset BIBREF22 and involves an English-language punctuation restoration task. This experiment demonstrates that our model is generalizable to different languages. Our model, which does not require pretrained word vectors, improved the overall F1 score by 0.9% compared to the baselines, including a model that uses pretrained word vectors. There are five sections in the remainder of this paper. Section SECREF2 reviews the related works on Thai sentence segmentation, English punctuation restoration and introduces the original CVT. Section SECREF3 describes the proposed model architecture and the integration of cross-view training. The datasets, implementation process and evaluation metrics are explained in Section SECREF4 . The results of the experiments are discussed in Section SECREF5 . Finally, Section SECREF6 concludes the paper. Related Works This section includes three subsections. The first subsection concerns Thai sentence segmentation, which is the main focus of this work. The task of English punctuation restoration, which is similar to our main task, is described in the second subsection. The last subsection describes the original Cross-View Training initially proposed in BIBREF20 . Thai sentence segmentation In Thai, texts do not contain markers that definitively identify sentence boundaries. Instead, written Thai usually uses a space as the vital element that separates text into sentences. However, there are three ways that spaces are used in this context BIBREF23 : before and after an interjection, before conjunctions, and before and after numeric expressions. Therefore, segmenting text into sentences cannot be performed simply by splitting a text at the spaces. Previous works from BIBREF5 , BIBREF6 , BIBREF7 have focused on disambiguating whether a space functions as the sentence boundary. These works extract contextual features from words and POS around the space. Then, the obtained features around the corresponding space are input into traditional models to predict whether space is a sentence boundary. Although a space is usually considered essential as a sentence boundary marker, approximately 23% of the sentences end without a space character in one news domain corpus BIBREF8 . Hence, Zhou N. et al. BIBREF8 proposed a word sequence tagging CRF-based model in which all words can be considered as candidates for the sentence boundary. A space is considered as only one possible means of forming a sentence boundary. The CRF-based model BIBREF24 , which is extracted from n-grams around the considered word, achieves a F1 score of 91.9%, which is approximately 10% higher than the F1 scores achieved by other models BIBREF5 , BIBREF6 , BIBREF7 on the Orchid dataset, as mentioned in BIBREF8 . In this work, we adopt the concept of word sequence tagging and compare it with two baselines: the CRF-based model with n-gram embedding, which is currently the state-of-the-art for Thai sentence segmentation, and the Bi-LSTM-CRF model, which is currently the deep learning state-of-the-art approach for sequence tagging. English punctuation restoration Most languages use a symbol that functions as a sentence boundary; however, a few do not use sentence markers including Thai, Lao and Myanmar. Thus, few studies have investigated sentence segmentation in raw text. However, studies on sentence segmentation, which is sometimes called sentence boundary detection, are still found in the speech recognition field BIBREF25 . The typical input to speech recognition model is simply a stream of words. If two sentences are spoken back to back, by default, a recognition engine will treat it as one sentence. Thus, sentence boundary detection is also considered a punctuation restoration task in speech recognition because when the model attempts to restores the period in the text, the sentence boundary position will also be defined. Punctuation restoration not only provides a minimal syntactic structure for natural language processing, similar to sentence boundary detection but also dramatically improves the readability of transcripts. Therefore, punctuation restoration has been extensively studied. Many approaches have been proposed for punctuation restoration that use different features, such as audio and textual features. Moreover, punctuation restoration is also considered to be a different machine learning problem, such as word sequence tagging and machine translation. A combination of audio and textual features were utilized in BIBREF26 , BIBREF27 , BIBREF28 to predict and restore punctuation, including pitch, intensity and pause duration, between words. We ignore these features in our experiment because our main task—Thai sentence segmentation— does not include audio features. Focusing only on textual features, there are two main approaches, namely, word sequence tagging and machine translation. For the machine translation approach, punctuation is treated as just another type of token that needs to be recovered and included in the output. The methods in BIBREF29 , BIBREF30 , BIBREF31 restore punctuation by translating from unpunctuated text to punctuated text. However, our main task, sentence segmentation, is an upstream task in text processing, unlike punctuation restoration, which is considered a downstream task. Therefore, the task needs to operate rapidly; consequently, we focus only on the sequence tagging model, which is less complex than the machine translation model. In addition to those machine translation tasks, both traditional approaches and deep learning approaches must solve a word sequence tagging problem. Of the traditional approaches, contextual features around the considered word were used to predict following punctuation in the n-gram BIBREF32 and CRF model approaches BIBREF33 , BIBREF34 . Meanwhile, in the deep learning approaches, a deep convolutional neural network BIBREF35 , T-LSTM (Textual-LSTM) BIBREF26 and a bidirectional LSTM model with an attention mechanism, called T-BRNN BIBREF36 , have been adopted to predict a punctuation sequence from the word sequence. T-BRNN BIBREF36 was proposed to solve the task as a word-sequence tagging problem, and it is currently the best model that uses the word sequence tagging approach. Tilk O. et al. BIBREF36 also proposed a variant named T-BRNN-pre, which integrates pretrained word vectors to improve the accuracy. To demonstrate that our model is generalizable to other languages, we compare it with other punctuation restoration models, including T-LSTM, T-BRNN, and T-BRNN-pre. These models adopt a word sequence tagging approach and do not utilize any prosodic or audio features. Cross-View Training CVT BIBREF20 is a semi-supervised learning technique whose goal is to improve the model representation using a combination of labeled and unlabeled data. During training, the model is trained alternately with one mini-batch of labeled data and INLINEFORM0 mini-batches of unlabeled data. Labeled data are input into the model to calculate the standard supervised loss for each mini-batch and the model weights are updated regularly. Meanwhile, each mini-batch of unlabeled data is selected randomly from the pool of all unlabeled data; the model computes the loss for CVT from the mini-batch of unlabeled data. This CVT loss is used to train auxiliary prediction modules, which see restricted views of the input, to match the output of the primary prediction module, which is the full model that sees all the input. Meanwhile, the auxiliary prediction modules share the same intermediate representation with the primary prediction module. Hence, the intermediate representation of the model is improved through this process. Similar to the previous work, we also apply CVT to a sequence tagging task. However, our model is composed of self-attention and CRF modules, which were not included in the sequence tagging model in BIBREF20 . The previous CVT was conducted on an LSTM using the concepts of forward and backward paths, which are not intuitively acquired by the self-attention model. Moreover, the output used to calculate CVT loss was generated by the softmax function, which does not operate with CRF. Thus, in our study, both the primary and auxiliary prediction modules needed to be constructed differently from the original ones. architecture/Semi As discussed in Section SECREF3 , CVT requires primary and auxiliary prediction modules for training with unlabeled data to improve the representation. Thus, we construct both types of prediction modules for our model. The flow of unlabeled data, which is processed to obtain a prediction by each module, is shown in Fig. . The output of each prediction module is transformed into the probability distribution of each class by the softmax function and then used to calculate INLINEFORM0 , as shown in cvtloss. DISPLAYFORM0 The INLINEFORM0 value is based on the Kullback–Leibler divergence (KL divergence) between the probability distribution of the primary INLINEFORM1 output and those of two auxiliary modules, INLINEFORM2 and INLINEFORM3 , where INLINEFORM4 . The KL divergence at each timestep is averaged when the timesteps are dropped timesteps D, which is described in Section UID25 . The details of the primary and auxiliary prediction modules, which are used in the INLINEFORM5 calculation, are described in the following subsections. In BIBREF20 , the output of the primary prediction module is acquired from the last layer and used to predict tags. However, our model uses a CRF layer to decode the tags, instead of the softmax function, whose input is the output from the last fully connected layer. Thus, the probability distribution of a primary prediction module should be the marginal probability acquired from the CRF layer. Nevertheless, the forward-backward algorithm for the marginal probability calculation is time-consuming with a time complexity is INLINEFORM0 , where INLINEFORM1 is the sequence length, and INLINEFORM2 is the number of tags. To reduce the training time, the probability distribution of the primary prediction module INLINEFORM3 is instead obtained from the output of the Softmax function, whose input is a virtual logit vector INLINEFORM4 , as shown in primary. DISPLAYFORM0 Two auxiliary views are included to improve the model. The first view is generated from a recurrent representation vector INLINEFORM0 to acquire the local probability distribution INLINEFORM1 , where INLINEFORM2 . The second view is generated from the low-level distant representation vectors INLINEFORM3 to acquire the probability distribution of a distant structure in the low-level module INLINEFORM4 , where INLINEFORM5 . By generating the views from these representation vectors separately, the local and distant structures in the low-level module can improve equally. Although both representation vectors are used separately to create auxiliary views, the input of each structure is still not restricted, unlike BIBREF20 , where the input is restricted to only previous or future tokens. Because BERT, which is trained by the masked language model, outperforms OpenAI GPT, which uses an autoregressive approach for training as reported in BIBREF14 , we adopt the concept of the masked language model BIBREF37 to obtain both auxiliary views. This approach allows the representation to fuse the left and the right context, which results in a better representation. By using the masked language model, some tokens at each timestep are randomly dropped and denoted as removed tokens INLINEFORM0 ; then, the remaining tokens are used to obtain auxiliary predictions in the dropped timesteps INLINEFORM1 , as shown in Fig. . The details of both auxiliary prediction modules are described below. architecture/Masklanguage For recurrent representation vectors, if one of the tokens is dropped, the related n-gram tokens that include the dropped tokens will also be dropped. For example, if INLINEFORM0 is dropped, INLINEFORM1 and INLINEFORM2 will also be dropped as removed tokens in the case of a bigram. The remaining n-gram tokens are then used to obtain the recurrent representation vectors at the dropped timesteps. Then, the vectors are provided as an input to the softmax function to obtain the probability distribution of the first auxiliary prediction module, as shown in auxlocal. DISPLAYFORM0 In the other auxiliary prediction module, a sequence of the low-level distant representation vectors is generated and some tokens are dropped. This sequence of vectors is also input into the Softmax function, just as in the first auxiliary prediction module, and the output is another probability distribution, which is the second auxiliary prediction, as shown in auxdistant. DISPLAYFORM0 Proposed method In this section, we describe our proposed method in two subsections. The first subsection specifies the model architecture and the details of each module. Our first and second contributions, which are local and distant representations, are mainly described in this subsection. Meanwhile, the second subsection expounds on how the model is trained with unlabeled data through the modified CVT, which is our third contribution. Model architecture In this work, the model predicts the tags INLINEFORM0 for the tokens in a word sequence INLINEFORM1 where INLINEFORM2 is the sequence size and INLINEFORM3 , INLINEFORM4 denote the token and its tag at timestep INLINEFORM5 , respectively. Each token INLINEFORM6 consists of a word, its POS and its type. There are five defined word types: English, Thai, punctuation, digits, and spaces. The tag set INLINEFORM0 is populated based on the considered task. In Thai sentence segmentation, the assigned tags are INLINEFORM1 and INLINEFORM2 ; INLINEFORM3 denotes that the corresponding word is a sentence boundary considered as the beginning of a sentence, while and INLINEFORM4 denotes that the word is not a sentence boundary. Meanwhile, there are four tags in the punctuation restoration task. Words not followed by any punctuation are tagged with INLINEFORM5 . Words that are followed by a period “.”, comma “,” or question mark “?” are tagged to INLINEFORM6 , INLINEFORM7 , and INLINEFORM8 , respectively. architecture/MainArchitecture Our model architecture is based on Bi-LSTM-CRF, as shown in Fig. . The model is divided into three modules. The first, low-level module, consists of two separate structures: local and distant structures. The second, high-level module, contains a sequence of stacked bidirectional LSTM and self-attention layers. The final module, the prediction module, is responsible for predicting the tags INLINEFORM0 . Each module is described more completely in the next three subsections. A sequence of word tokens is input into the low-level module. The input tokens pass through two structures. The first structure generates a sequence of local representation vectors INLINEFORM0 , and the second structure generates low-level distant representation vectors INLINEFORM1 . After obtaining both sequences of representation vectors, the local representation vectors are fed to the Bi-LSTM to obtain the recurrent representation vectors INLINEFORM2 , as shown in recurrentrep. Then, the recurrent and distant representation vectors are concatenated to form the low-level representation vector INLINEFORM3 , as shown in low-level: DISPLAYFORM0 DISPLAYFORM1 This structure is shown as the left submodule of the low-level module in Fig. . It extracts the local representation vectors INLINEFORM0 . Its input tokens are used to create n-gram tokens, which are unigrams INLINEFORM1 , bigrams INLINEFORM2 , and trigrams INLINEFORM3 . Each n-gram token is represented as an embedding vector, which is classified as a unigram embedding vector INLINEFORM4 , a bigram embedding vector INLINEFORM5 or a trigram embedding vector INLINEFORM6 . Each vector INLINEFORM7 is mapped from a token by gram embedding INLINEFORM8 , which is a concatenated vector of the word embedding INLINEFORM9 , POS embedding INLINEFORM10 and type embedding INLINEFORM11 , as shown in eq:embedding: DISPLAYFORM0 Each n-gram token at timestep INLINEFORM0 is generated by the previous, present and next token and embedded into vectors as shown in uni,bi,tri. The unigram embedding at timestep INLINEFORM1 is a unigram embedding of the current token INLINEFORM2 . The bigram embedding vector at timestep INLINEFORM3 is a bigram embedding of the previous and present tokens INLINEFORM4 , and the trigram embedding vector at timestep INLINEFORM5 is a trigram embedding of the previous, present and next tokens INLINEFORM6 : DISPLAYFORM0 DISPLAYFORM1 At each timestep INLINEFORM0 , a local representation vector INLINEFORM1 is combined from the n-gram embedding vectors generated from the context around INLINEFORM2 . A combination of embedding vectors, which is used to construct a local representation vector, is shown in n-gramcombination. A combination consists of the unigram, bigram, and trigram embedding vectors at timesteps INLINEFORM3 , INLINEFORM4 and INLINEFORM5 and it is a concatenation of all the embedding vectors: DISPLAYFORM0 The distant structure, which is a self-attention module, is shown in Fig. on the right side of the low-level module. The structure extracts low-level distant representation vectors INLINEFORM0 from a sequence of unigram embedding vectors INLINEFORM1 , as shown in distantattention. In this case, the self-attention module is a scaled dot-product attention BIBREF11 , where key, query, and value vectors are the linear projections of the unigram embedding vectors shown in Fig. . The linear transformations for key, query, and value are learned separately and updated in the model through backpropagation. The output vector, which is the scaled dot-product attention at each timestep, is concatenated with the input vector INLINEFORM2 and projected by a linear transformation. That projected vector is the output vector of a self-attention module, which is a low-level distant representation vector. architecture/selfattention DISPLAYFORM0 The low-level representation vectors INLINEFORM0 are used as the input for this module, which outputs the high-level representation vectors INLINEFORM1 whose calculation is shown in high-level. The high-level module, as shown in Fig. , is composed of a stacked bidirectional LSTM and a self-attention modules. A stacked bidirectional LSTM contains K layers of bidirectional LSTMs in which the output from the previous bidirectional LSTM layer is the input of the next bidirectional LSTM layer. The self-attention part of this structure is the same as that in the low-level distant structure. The self-attention module helps to generate the high-level distant representation vectors that are output by the high-level module. DISPLAYFORM0 The prediction module is the last module. It includes two layers: a fully connected layer and a CRF layer. In the fully connected layer, the output vectors from the high-level module are projected by a linear transformation as shown in virtual. The purpose of this layer is to create the virtual logit vectors INLINEFORM0 , which represent the probability distribution for CVT, as discussed in Section SECREF21 . Therefore, the number of dimensions of logits equals the number of possible tags in each task: DISPLAYFORM0 The CRF layer is responsible for predicting the tag INLINEFORM0 of a token at each timestep, as shown in crf. The layer receives a sequence of virtual logit vectors ( INLINEFORM1 ) as input and then decodes them to a sequence of tags INLINEFORM2 using the Viterbi algorithm. DISPLAYFORM0 Datasets Three datasets are used in the experiments as described in the following subsections. We use two datasets for Thai sentence segmentation, and the third dataset is used for English punctuation restoration. The statistics of the preprocessed data are shown in Table TABREF31 , including the number of sequences and the number of vocabulary words in each dataset. We also calculate the average number of words per passage in the unlabeled data that do not appear in the labeled data, as shown in Table TABREF32 . This dataset is a Thai part-of-speech-tagged dataset containing 10,864 sentences. In the corpus, text was separated into paragraphs, sentences, and words hierarchically by linguists. Each word was also manually assigned a POS by linguists. These data include no unlabeled data with the same word segmentation and POS tag set. Hence, we do not execute CVT on this dataset. Our data preprocessing on the ORCHID corpus was similar to that in BIBREF8 : all the comments are removed, and the data are partitioned into 10 parts containing equal numbers of sentences to support 10-fold cross-validation. Each training set is split into one part used for validation and the rest is used for model training. Subsequently, all the words in each dataset are concatenated and then separated into sequences with 200 words per instance. Each sequence always begins with the first word of a sentence. If a sequence ends with an unfinished sentence, the next sequence starts with that complete sentence. This Thai dataset includes many types of labeled data useful in sentence segmentation tasks. The raw text was generated by users having conversations in the financial domain and were acquired mainly by crawling social sites. The labeled data for sentence segmentation were manually annotated by linguists using the definitions in BIBREF21 . At the time of this study, the dataset was extended from that in BIBREF21 ; the data were collected from January 2017 to December 2017. The labeled dataset includes 48,374 passages. To support semi-supervised learning, the first 3 months of data (96,777 passages) are unlabeled. Because the data stem from social media, some text exists that cannot be considered as part of any sentence, such as product links, symbols unrelated to sentences, and space between sentences. These portions were not originally annotated as sentences by the linguists. However, in this work, we treat these portions as individual sentences and tag the first word of each fraction as the sentence boundary. For evaluation purposes, the collection of passages in this dataset is based on 5-fold cross-validation, similar to the previous work BIBREF21 . The passages are treated as input sequences for the model. For each passage, word segmentation and POS tagging are processed by the custom models from this dataset. We adopted this English-language dataset to enable comparisons with models intended for other languages. The dataset is composed of TED talk transcripts. To compare our model with those of previous works, we selected the training dataset for the machine translation track in IWSLT2012 and separated it into training and validation sets containing 2.1 million and 295 thousand words, respectively. The testing dataset is the IWSLT2011 reference set, which contains 13 thousand words. To acquire unlabeled data for semi-supervised learning, we adopted the IWSLT2016 machine translation track training data; duplicate talks that also appear in IWSLT2012 are discarded. The data preprocessing follows the process in BIBREF36 . Each sequence is generated from 200 words, of which beginning is always the first word in a sentence. If a sentence is cut at the end of a sequence, that sentence is copied in full to the beginning of the next sequence. To use our model, the POS of each word is required. However, the IWSLT dataset contains only the raw text of transcripts and does not include POS tags. Thus, we implement POS tagging using a special library BIBREF38 to predict the POS of each word. Implementation Detail Before mapping each token included in the unigram, bigram, and trigram to the embedding vector, we limit the minimum frequency of occurring words that are not marked as an unknown token. There are 2 parameters set for the unigram INLINEFORM0 and the remaining INLINEFORM1 , respectively. We found that model accuracy is highly sensitive to these parameters. Therefore, we use a grid search technique to find the best value for both parameters for the model. We apply two optimizers used in this work: Adagard BIBREF39 and Adam BIBREF40 , whose learning rates are set to 0.02 and 0.001 for the Thai and English datasets, respectively. To generalize the model, we also integrate L2 regularization with an alpha of 0.01 to the loss function for model updating. Moreover, dropout is applied to the local representation vectors, recurrent representation vectors, between all bidirectional LSTMs and enclosed by the self-attention mechanism in the high-level module. During training, both the supervised and semi-supervised models are trained until the validation metrics stop improving; the metrics are (1) sentence boundary F1 score and (2) overall F1 score for Thai sentence segmentation and English punctuation restoration, respectively. CVT has three main parameters that impact model accuracy. The first is the drop rate of the masked language model, which determines the number of tokens that are dropped and used for learning auxiliary prediction modules as described in Section SECREF21 . The second is the number of unlabeled mini-batches INLINEFORM0 used for training between supervised mini-batches. Third, rather than using the same dropout rate for the local representation vectors, a new dropout rate is assigned. The details of hyperparameters such as the hidden size of each layer and dropout rate are given in Section SECREF7 . Evaluation During the evaluation, each task is assessed using different metrics based on previous works. For Thai sentence segmentation, three metrics are used in the evaluation: sentence boundary F1 score, non-sentence boundary F1 score, and space correct BIBREF8 . In this work, we mainly focus on the performance of sentence boundary prediction and not non-sentence boundary prediction or space prediction. Therefore, we make comparisons with other models regarding only their sentence boundary F1 scores. The equation for the sentence boundary F1 score metric is shown in f1sb. In calculating the F1 score, the positive class is defined as the sentence boundary, and the negative class is defined as the non-sentence boundary. INLINEFORM0 INLINEFORM1 For English punctuation, the evaluation is measured on each type of punctuation and overall F1 score. For the punctuation restoration task, we care only about the performance of the samples belonging to the classes that are tagged to words followed by punctuation; therefore class INLINEFORM0 , which represents words not immediately followed by punctuation, is ignored in the evaluation. Consequently, the overall F1 score does not include INLINEFORM1 as the positive class in f1overall. INLINEFORM2 INLINEFORM3 To compare the performance of each punctuation restoration model in a manner similar to sentence segmentation, the 2-class F1 score is calculated to measure model accuracy, as shown in f12class. The calculation of this metric is the same as that used in BIBREF35 . The metric considers only where the punctuation position is and ignores the type of restored punctuation. Therefore, this measure is similar to the metric sentence boundary F1, which only considers the position of the missing punctuation. INLINEFORM0 INLINEFORM1 Results and discussions We report and discuss the results of our two tasks in four subsections. The first and second subsections include the effect of local representation and distant representation, respectively. The impact of CVT is explained in the third subsection. The last subsection presents a comparison of our model and all the baselines. Moreover, we also conduct paired t-tests to investigate the significance of the improvement from each contribution, as shown in Section SECREF9 . Effect of local representation To find the effect of local representation, we compare a standard Bi-LSTM-CRF model using our full implementation to the model that includes n-gram embedding to extract local representation. In tab:thairesult,tab:engresult, the standard Bi-LSTM-CRF model is represented as Bi-LSTM-CRF (row (e)), while the models with local features are represented as INLINEFORM0 (row (f)). The results in Table TABREF45 show that using n-gram to obtain the local representation improves the F1 score of the model from 90.9% (row (e)) to 92.4% (row (f)) on the Orchid dataset and from 87.6% (row (e)) to 88.7% (row (f)) on the UGWC dataset. These results occur because many word groups exist that can be used to signal the beginning and end of a sentence in Thai. Word groups always found near sentence boundaries can be categorized into 2 groups. The first group consists of final particles, e.g., “thaiนะ|คะ ” (na | kha), “thaiนะ|ครับ ” (na | khrạb), “thaiเลย|ครับ ” (ley | khrạb), “thaiแล้ว|ครับ ” (laêw | khrạb), and others. These word groups are usually used at the ends of sentences to indicate the formality level. For instance, the model with local representation can detect the sentence boundary at “thaiครับ ” (khrạb) that is followed by “thaiแล้ว ” (laêw), as shown in Fig. , while the model without local representation cannot detect the word as a sentence boundary. The second group consists of conjunctions that are always used at the beginnings of sentences, e.g., “thaiจาก|นั้น (after that) ”, “thaiไม่|งั้น (otherwise) ” and others. The model that uses n-gram to capture word group information is better able to detect word groups near sentence boundaries. Thus, this model can identify these sentence boundaries easily in the Thai language. result/cherrypickngram In contrast, for the English dataset, local representation using n-gram drops the overall F1 score of punctuation restoration from 64.4% (row (e)) to 63.6% (row (f)), as shown in Table TABREF47 . However, the 2-class F1 score increases slightly from 81.4% (row (e)) to 81.8% (row (f)) when compared to the Bi-LSTM-CRF model, which does not integrate n-gram embedding. Common phrases such as ”In spite of”, ”Even though” and ”Due to the fact” might provide strong cues for punctuation; however, such phrases can be found at both the beginnings and in the middle of sentences. Because such phrases can be used in both positions, they may follow commas when they are in the middle of the sentence or periods when they are at the beginning of a sentence. However, they still follow either a period or a comma; consequently, such phrases can still help identify whether the punctuation should be restored, which increases the 2-class F1 score, which considers only the positions of missing punctuation. Moreover, English does not use the concept of a final particle usually found at the end of the sentence—similar to the Thai word group mentioned earlier—including “thaiนะ|คะ ”(na | kha), “thaiนะ|ครับ ”(na | khrạb), “thaiเลย|ครับ ” (ley|khrạb), “thaiแล้ว|ครับ ” (laêw | khrạb) and others. Therefore, the word groups captured by n-gram can only help to identify where punctuation should be restored but they do not help the model determine the type of punctuation that should be restored. Effect of distant representation The effect of this contribution can be found by comparing the model that integrates the distant representation and the model that does not. The model with distant features integrated is represented as INLINEFORM0 (row (g)) in both tables. In this case, the distant representation is composed of the self-attention modules in both the low- and high-level modules, as shown in Fig. . From the combination of local and distant representation, the results in tab:thairesult,tab:engresult show that the distant feature improves the accuracy of the model on all datasets compared to the model with no distant representation. The F1 scores of the sentence segmentation models improved slightly, from 92.4% and 88.7% (row (f)) to 92.5% and 88.8% (row (g)) on the Orchid and UGWC datasets, respectively. For the IWSLT dataset, the distant feature can recover the overall F1 score of punctuation restoration, which is degraded by the n-gram embedding; it improves from 63.6% (row (f)) to 64.5% (row (g)). The reason is that the self-attention modules focus selectively on certain parts of the passage. Thus, the model focuses on the initial words of the dependent clauses, which helps in classifying which type of punctuation should be restored. An example is shown in Fig. : the model with distant representation classifies the punctuation after ”her” as a ”COMMA” because ”Before” is the word that indicates the dependent clause. Meanwhile, the model without distant representation predicts the punctuation as a ”PERIOD” because there is no self-attention module; therefore, it does not focus on the word ”Before”. Overall, the model that includes both local and distant representation can generally be used for both sentence segmentation and punctuation restoration, and it outperforms both baseline models. result/cherrypickdistant Effect of Cross-View Training (CVT) To identify the improvement from CVT, we compared the models that use different training processes: standard supervised training ( INLINEFORM0 ) and CVT ( INLINEFORM1 ). The model trained with CVT improves the accuracy in terms of the F1 score on both Thai and English datasets, as shown in tab:thairesult,tab:engresult (row (g) vs row (h)). This experiment was conducted only on the UGWC dataset because no unlabeled data are available in the Orchid dataset, as mentioned in Section UID33 . The model improves the F1 score slightly, from 88.8% (row (g)) to 88.9% (row (h)) on the UGWC dataset. This result occurs because both the labeled and unlabeled data in the UGWC dataset are drawn from the same finance domain. The average number of new words found in a new unlabeled data passage is only 0.650, as shown in Table TABREF32 . Therefore, there is little additional information to be learned from unlabeled data. CVT also improved the model on the IWSLT dataset, from an overall F1 score of 64.5% (row (g)) to 65.3% (row (h)) and from a 2-class F1 score of 81.7% to 82.7%. Because both the labeled and unlabeled data were collected from TED talks, the number of vocabulary words grows substantially more than in the UGWC dataset because the talks cover various topics. In this dataset, average 1.225 new words found in each new unlabeled data passage, as shown in Table TABREF32 ; consequently the model representation learns new information from these new words effectively. Comparison with baseline models For the Thai sentence segmentation task, our model is superior to all the baselines on both Thai sentence segmentation datasets, as shown in Table TABREF45 . On the Orchid dataset, the supervised model that includes both local and distant representation was adopted for comparison to the baseline model. Our model improves the F1 score achieved by CRF-ngram, which is the state-of-the-art model for Thai sentence segmentation in Orchid, from 91.9% (row (d)) to 92.5% (row (g)). Meanwhile, in the UGWC dataset, our CVT model (row (h)) achieves an F1 score of 88.9%, which is higher than the F1 score of both the baselines (CRF-ngram and Bi-LSTM-CRF (rows d and e, respectively)). Thus, our model is now the state-of-the-art model for Thai sentence segmentation on both the Orchid and UGWC datasets. Our model outperforms all the sequence tagging models. T-BRNN-pre (row (c)) is the current state-of-the-art model, as shown in Table TABREF47 . The CVT model improves the overall F1 score from the 64.4% of T-BRNN-pre to 65.3% (row (h)), despite the fact that T-BRNN-pre integrates a pretrained word vector. Moreover, our model also achieves a 2-class F1 score 1.3% higher than that of Bi-LSTM-CRF (row (e)). Conclusions In this paper, we propose a novel deep learning model for Thai sentence segmentation. This study makes three main contributions. The first contribution is to integrate a local representation based on n-gram embedding into our deep model. This approach helps to capture word groups near sentence boundaries, allowing the model to identify boundaries more accurately. Second, we integrate a distant representation obtained from self-attention modules to capture sentence contextual information. This approach allows the model to focus on the initial words of dependent clauses (i.e., ”Before”, ”If”, and ”Although”). The last contribution is an adaptation of CVT, which allows the model to utilize unlabeled data to produce effective local and distant representations. The experiment was conducted on two Thai datasets, Orchid and UGWC, and one English punctuation restoration dataset, IWSLT. English punctuation restoration is similar to our Thai sentence segmentation. On the Thai sentence segmentation task, our model achieves F1 scores of 92.5% and 88.9% on the Orchid and UGWC datasets, constituting a relative error reduction of 7.4% and 10.5%, respectively. On the English punctuation task, the 2-class F1 score reached 82.7% when considering only two punctuation classes (making the task similar to sentence segmentation in Thai). Moreover, our model outperforms the model integrated with pretrained word vectors in terms of the overall F1 score on the IWSLT dataset. Based on our contributions, the local representation scheme has the highest impact on the Thai corpus, while the distant representation and CVT result in strong improvements on the English dataset. Moreover, our model can also be applied to elementary discourse unit (EDU) segmentation, which is used as the minimal syntactic unit for downstream tasks such as text summarization and machine translation. However, no experiments have been conducted to determine how different sentences and EDUs affect downstream tasks. Therefore, the evaluation of downstream tasks from different sources needs to be studied. Acknowledgment This paper was supported by KLabs at Kasikorn Business Technology (KBTG), who provided facilities and data. The procedures that were conducted based on social data are visible to the public, and ethical issues that can arise from the use of such data were addressed. We would like to thank the linguists Sasiwimon Kalunsima, Nutcha Tirasaroj, Tantong Champaiboon and Supawat Taerungruang for annotating the UGWC dataset used in this study. Hyperparameters The hyperparameter values were determined through a grid search to find their optimal values on the different datasets. All the hyperparameters for each dataset are shown in Table TABREF55 . The optimal values from the grid search depend on the task. For Thai sentence segmentation, the hyperparameters are tuned to obtain the highest sentence boundary F1 score, while the overall F1 score is used to tune the parameters for English punctuation restoration. Comparison of CNN and n-gram models for local representation Jacovi A. et al. BIBREF13 proposed that a CNN can be used as an n-gram detector to capture local text features. Therefore, we also performed an experiment to compare a CNN and n-gram embedded as local structures. The results in Table TABREF56 show that the model using the embedded n-gram yields greater improvement than the one using an embedded CNN on the Orchid and UGWC datasets. Statistical Tests for Thai sentence segmentation To prove the significance of the model improvements, we compared the cross-validation results using paired t-tests to obtain the p-values, which are shown in Table TABREF57 for the Orchid dataset and Table TABREF58 for the UGWC dataset.
How do they utilize unlabeled data to improve model representations?
e0e379e546f1da9da874a2e90c79b41c60feb817
How Ġdo Ġthey Ġutilize Ġunl abel ed Ġdata Ġto Ġimprove Ġmodel Ġrepresentations ?
During Ġtraining , Ġthe Ġmodel Ġis Ġtrained Ġaltern ately Ġwith Ġone Ġmini - batch Ġof Ġlabeled Ġdata Ġand ĠIN LINE FORM 0 Ġmini - bat ches Ġof Ġunl abel ed Ġdata .
<s> How Ġdo Ġthey Ġutilize Ġunl abel ed Ġdata Ġto Ġimprove Ġmodel Ġrepresentations ? </s> Introduction </s> Aut omatic Ġsummar ization , Ġmachine Ġtranslation , Ġquestion Ġanswering , Ġand Ġsemantic Ġparsing Ġoperations Ġare Ġuseful Ġfor Ġprocessing , Ġanalyzing , Ġand Ġextracting Ġmeaningful Ġinformation Ġfrom Ġtext . ĠHowever , Ġwhen Ġapplied Ġto Ġlong Ġtexts , Ġthese Ġtasks Ġusually Ġrequire Ġsome Ġminimal Ġsynt actic Ġstructure Ġto Ġbe Ġidentified , Ġsuch Ġas Ġsentences ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ, Ġwhich Ġalways Ġend Ġwith Ġa Ġperiod Ġ( âĢ ľ . âĢ Ŀ ) Ġin ĠEnglish ĠB IB REF 3 Ġ. </s> However , Ġwritten ĠThai Ġdoes Ġnot Ġuse Ġan Ġexplicit Ġend - of - sent ence Ġmarker Ġto Ġidentify Ġsentence Ġboundaries ĠB IB REF 4 Ġ. ĠPrior Ġworks Ġhave Ġadapted Ġtraditional Ġmachine Ġlearning Ġmodels Ġto Ġpredict Ġthe Ġbeginning Ġposition Ġof Ġa Ġsentence . ĠThe Ġauthors Ġof ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġproposed Ġtraditional Ġmodels Ġto Ġdetermine Ġwhether Ġa Ġconsidered Ġspace Ġis Ġa Ġsentence Ġboundary Ġbased Ġon Ġthe Ġwords Ġand Ġtheir Ġpart Ġof Ġspeech Ġ( POS ) Ġnear Ġthe Ġspace . ĠMeanwhile , ĠZhou ĠN . Ġet Ġal . ĠB IB REF 8 Ġconsidered ĠThai Ġsentence Ġsegment ation Ġas Ġa Ġsequence Ġtagging Ġproblem Ġand Ġproposed Ġa ĠCR F - based Ġmodel Ġwith Ġn - gram Ġembed ding Ġto Ġpredict Ġwhich Ġword Ġis Ġthe Ġsentence Ġboundary . ĠThis Ġmethod Ġachieves Ġthe Ġstate - of - the - art Ġresult Ġfor ĠThai Ġsentence Ġsegment ation Ġand Ġachieves Ġgreater Ġaccuracy Ġthan Ġother Ġmodels Ġby Ġapproximately Ġ10 % Ġon Ġan ĠOr chid Ġdataset ĠB IB REF 9 Ġ. </s> Several Ġdeep Ġlearning Ġapproaches Ġhave Ġbeen Ġapplied Ġin Ġvarious Ġtasks Ġof Ġnatural Ġlanguage Ġprocessing Ġ( N LP ), Ġincluding Ġthe Ġlong Ġshort - term Ġmemory ĠB IB REF 10 Ġ, Ġself - att ention ĠB IB REF 11 Ġ, Ġand Ġother Ġmodels . ĠHuang ĠZ . Ġet Ġal . ĠB IB REF 12 Ġproposed Ġa Ġdeep Ġlearning Ġsequence Ġtagging Ġmodel Ġcalled ĠBi - L ST M - CR F , Ġwhich Ġintegrates Ġa Ġconditional Ġrandom Ġfield Ġ( CR F ) Ġmodule Ġto Ġgain Ġthe Ġbenefit Ġof Ġboth Ġdeep Ġlearning Ġand Ġtraditional Ġmachine Ġlearning Ġapproaches . ĠIn Ġtheir Ġexperiments , Ġthe ĠBi - L ST M - CR F Ġmodel Ġachieved Ġan Ġimproved Ġlevel Ġof Ġaccuracy Ġin Ġmany ĠN LP Ġsequence Ġtagging Ġtasks , Ġsuch Ġas Ġnamed Ġentity Ġrecognition , ĠPOS Ġtagging Ġand Ġchunk ing . </s> The ĠCR F Ġmodule Ġachieved Ġthe Ġbest Ġresult Ġon Ġthe ĠThai Ġsentence Ġsegment ation Ġtask ĠB IB REF 8 Ġ; Ġtherefore , Ġwe Ġadopt Ġthe ĠBi - L ST M - CR F Ġmodel Ġas Ġour Ġbaseline . ĠThis Ġpaper Ġmakes Ġthe Ġfollowing Ġthree Ġcontributions Ġto Ġimprove ĠBi - L ST M - CR F Ġfor Ġsentence Ġsegment ation . </s> First , Ġwe Ġpropose Ġadding Ġn - gram Ġembed ding Ġto ĠBi - L ST M - CR F Ġdue Ġto Ġits Ġsuccess Ġin ĠB IB REF 8 Ġand ĠB IB REF 12 Ġ. ĠBy Ġincluding Ġn - gram Ġembed ding , Ġthe Ġmodel Ġcan Ġcapitalize Ġon Ġboth Ġapproaches . ĠFirst , Ġthe Ġmodel Ġgains Ġthe Ġability Ġto Ġextract Ġpast Ġand Ġfuture Ġinput Ġfeatures Ġand Ġsentence Ġlevel Ġtag Ġinformation Ġfrom ĠBi - L ST M - CR F ; Ġmoreover , Ġwith Ġthe Ġn - gram Ġaddition , Ġit Ġcan Ġalso Ġextract Ġa Ġlocal Ġrepresentation Ġfrom Ġn - gram Ġembed ding , Ġwhich Ġhelps Ġin Ġcapturing Ġword Ġgroups Ġthat Ġexist Ġnear Ġsentence Ġboundary . ĠAlthough ĠJac ovi ĠA . Ġet Ġal . ĠB IB REF 13 Ġreported Ġthat Ġa Ġconv olution al Ġneural Ġnetwork Ġ( CNN ) Ġcan Ġbe Ġused Ġas Ġan Ġn - gram Ġdetector Ġto Ġcapture Ġlocal Ġfeatures , Ġwe Ġchose Ġn - gram Ġembed ding Ġover Ġa ĠCNN Ġdue Ġto Ġits Ġbetter Ġaccuracy , Ġas Ġwill Ġbe Ġshown Ġin ĠSection ĠSEC REF 8 Ġ. </s> Second , Ġwe Ġpropose Ġadding Ġincorpor ative Ġdistant Ġrepresentation Ġinto Ġthe Ġmodel Ġvia Ġa Ġself - att ention Ġmechanism , Ġwhich Ġcan Ġfocus Ġon Ġthe Ġkeywords Ġof Ġdependent Ġclauses Ġthat Ġare Ġfar Ġfrom Ġthe Ġconsidered Ġword . ĠSelf - att ention Ġhas Ġbeen Ġused Ġin Ġmany Ġrecent Ġstate - of - the - art Ġmodels , Ġmost Ġnotably Ġthe Ġtransformer ĠB IB REF 11 Ġand ĠB ERT ĠB IB REF 14 Ġ. ĠB ERT Ġhas Ġoutper formed ĠBi - L ST M Ġon Ġnumerous Ġtasks , Ġincluding Ġquestion Ġanswering Ġand Ġlanguage Ġinference . ĠTherefore , Ġwe Ġchoose Ġto Ġuse Ġself - att ention Ġmodules Ġto Ġextract Ġdistant Ġrepresentations Ġalong Ġwith Ġlocal Ġrepresentations Ġto Ġimprove Ġmodel Ġaccuracy . </s> Third , Ġwe Ġalso Ġapply Ġsemi - super vised Ġlearning ĠB IB REF 15 Ġ, Ġallowing Ġus Ġto Ġemploy Ġunlimited Ġamounts Ġof Ġunl abel ed Ġdata , Ġwhich Ġis Ġparticularly Ġimportant Ġfor Ġlow - resource Ġlanguages Ġsuch Ġas ĠThai , Ġfor Ġwhich Ġannotation Ġis Ġcostly Ġand Ġtime - consuming . ĠMany Ġsemi - super vised Ġlearning Ġapproaches Ġhave Ġbeen Ġproposed Ġin Ġthe Ġcomputer Ġvision ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġand Ġnatural Ġlanguage Ġprocessing ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġfields . ĠOur Ġchoice Ġfor Ġsemi - super vised Ġlearning Ġto Ġenhance Ġmodel Ġrepresentation Ġis ĠCross - View ĠTraining Ġ( CV T ) ĠB IB REF 20 Ġ. ĠClark ĠK . Ġet Ġal . ĠB IB REF 20 Ġclaims Ġthat ĠCV T Ġcan Ġimprove Ġthe Ġrepresentation Ġlayers Ġof Ġthe Ġmodel , Ġwhich Ġis Ġour Ġgoal . ĠHowever , ĠCV T Ġwas Ġnot Ġdesigned Ġto Ġbe Ġintegrated Ġwith Ġself - att ention Ġand ĠCR F Ġmodules ; Ġconsequently , Ġwe Ġprovide Ġa Ġmodified Ġversion Ġof ĠCV T Ġin Ġthis Ġwork . </s> Based Ġon Ġthe Ġabove Ġthree Ġcontributions , Ġwe Ġpursue Ġtwo Ġmain Ġexperiments . ĠThe Ġfirst Ġexperiment Ġwas Ġconducted Ġon Ġtwo ĠThai Ġdatasets , ĠOr chid Ġand ĠU G WC ĠB IB REF 21 Ġ, Ġto Ġevaluate Ġour ĠThai Ġsentence Ġsegment ation Ġmodel . ĠIn Ġthis Ġcase , Ġour Ġmodel Ġachieves ĠF 1 Ġscores Ġof Ġ92 . 5 % Ġand Ġ88 . 9 % Ġon ĠOr chid Ġand ĠU G WC , Ġrespectively , Ġand Ġit Ġoutper forms Ġall Ġthe Ġbaseline Ġmodels . ĠThe Ġsecond Ġexperiment Ġwas Ġexecuted Ġon Ġthe ĠI W SL T Ġdataset ĠB IB REF 22 Ġand Ġinvolves Ġan ĠEnglish - language Ġpunct uation Ġrestoration Ġtask . ĠThis Ġexperiment Ġdemonstrates Ġthat Ġour Ġmodel Ġis Ġgeneral izable Ġto Ġdifferent Ġlanguages . ĠOur Ġmodel , Ġwhich Ġdoes Ġnot Ġrequire Ġpret rained Ġword Ġvectors , Ġimproved Ġthe Ġoverall ĠF 1 Ġscore Ġby Ġ0 . 9 % Ġcompared Ġto Ġthe Ġbas elines , Ġincluding Ġa Ġmodel Ġthat Ġuses Ġpret rained Ġword Ġvectors . </s> There Ġare Ġfive Ġsections Ġin Ġthe Ġremainder Ġof Ġthis Ġpaper . ĠSection ĠSEC REF 2 Ġreviews Ġthe Ġrelated Ġworks Ġon ĠThai Ġsentence Ġsegment ation , ĠEnglish Ġpunct uation Ġrestoration Ġand Ġintroduces Ġthe Ġoriginal ĠCV T . ĠSection ĠSEC REF 3 Ġdescribes Ġthe Ġproposed Ġmodel Ġarchitecture Ġand Ġthe Ġintegration Ġof Ġcross - view Ġtraining . ĠThe Ġdatasets , Ġimplementation Ġprocess Ġand Ġevaluation Ġmetrics Ġare Ġexplained Ġin ĠSection ĠSEC REF 4 Ġ. ĠThe Ġresults Ġof Ġthe Ġexperiments Ġare Ġdiscussed Ġin ĠSection ĠSEC REF 5 Ġ. ĠFinally , ĠSection ĠSEC REF 6 Ġconcludes Ġthe Ġpaper . </s> Related ĠWorks </s> This Ġsection Ġincludes Ġthree Ġsubsections . ĠThe Ġfirst Ġsubsection Ġconcerns ĠThai Ġsentence Ġsegment ation , Ġwhich Ġis Ġthe Ġmain Ġfocus Ġof Ġthis Ġwork . ĠThe Ġtask Ġof ĠEnglish Ġpunct uation Ġrestoration , Ġwhich Ġis Ġsimilar Ġto Ġour Ġmain Ġtask , Ġis Ġdescribed Ġin Ġthe Ġsecond Ġsubsection . ĠThe Ġlast Ġsubsection Ġdescribes Ġthe Ġoriginal ĠCross - View ĠTraining Ġinitially Ġproposed Ġin ĠB IB REF 20 Ġ. </s> Th ai Ġsentence Ġsegment ation </s> In ĠThai , Ġtexts Ġdo Ġnot Ġcontain Ġmarkers Ġthat Ġdefinitively Ġidentify Ġsentence Ġboundaries . ĠInstead , Ġwritten ĠThai Ġusually Ġuses Ġa Ġspace Ġas Ġthe Ġvital Ġelement Ġthat Ġseparates Ġtext Ġinto Ġsentences . ĠHowever , Ġthere Ġare Ġthree Ġways Ġthat Ġspaces Ġare Ġused Ġin Ġthis Ġcontext ĠB IB REF 23 Ġ: Ġbefore Ġand Ġafter Ġan Ġinter jection , Ġbefore Ġconj unctions , Ġand Ġbefore Ġand Ġafter Ġnumeric Ġexpressions . ĠTherefore , Ġsegment ing Ġtext Ġinto Ġsentences Ġcannot Ġbe Ġperformed Ġsimply Ġby Ġsplitting Ġa Ġtext Ġat Ġthe Ġspaces . </s> Previous Ġworks Ġfrom ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġhave Ġfocused Ġon Ġdis amb ig uating Ġwhether Ġa Ġspace Ġfunctions Ġas Ġthe Ġsentence Ġboundary . ĠThese Ġworks Ġextract Ġcontextual Ġfeatures Ġfrom Ġwords Ġand ĠPOS Ġaround Ġthe Ġspace . ĠThen , Ġthe Ġobtained Ġfeatures Ġaround Ġthe Ġcorresponding Ġspace Ġare Ġinput Ġinto Ġtraditional Ġmodels Ġto Ġpredict Ġwhether Ġspace Ġis Ġa Ġsentence Ġboundary . </s> Although Ġa Ġspace Ġis Ġusually Ġconsidered Ġessential Ġas Ġa Ġsentence Ġboundary Ġmarker , Ġapproximately Ġ23 % Ġof Ġthe Ġsentences Ġend Ġwithout Ġa Ġspace Ġcharacter Ġin Ġone Ġnews Ġdomain Ġcorpus ĠB IB REF 8 Ġ. ĠHence , ĠZhou ĠN . Ġet Ġal . ĠB IB REF 8 Ġproposed Ġa Ġword Ġsequence Ġtagging ĠCR F - based Ġmodel Ġin Ġwhich Ġall Ġwords Ġcan Ġbe Ġconsidered Ġas Ġcandidates Ġfor Ġthe Ġsentence Ġboundary . ĠA Ġspace Ġis Ġconsidered Ġas Ġonly Ġone Ġpossible Ġmeans Ġof Ġforming Ġa Ġsentence Ġboundary . ĠThe ĠCR F - based Ġmodel ĠB IB REF 24 Ġ, Ġwhich Ġis Ġextracted Ġfrom Ġn - gram s Ġaround Ġthe Ġconsidered Ġword , Ġachieves Ġa ĠF 1 Ġscore Ġof Ġ91 . 9 %, Ġwhich Ġis Ġapproximately Ġ10 % Ġhigher Ġthan Ġthe ĠF 1 Ġscores Ġachieved Ġby Ġother Ġmodels ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġon Ġthe ĠOr chid Ġdataset , Ġas Ġmentioned Ġin ĠB IB REF 8 Ġ. </s> In Ġthis Ġwork , Ġwe Ġadopt Ġthe Ġconcept Ġof Ġword Ġsequence Ġtagging Ġand Ġcompare Ġit Ġwith Ġtwo Ġbas elines : Ġthe ĠCR F - based Ġmodel Ġwith Ġn - gram Ġembed ding , Ġwhich Ġis Ġcurrently Ġthe Ġstate - of - the - art Ġfor ĠThai Ġsentence Ġsegment ation , Ġand Ġthe ĠBi - L ST M - CR F Ġmodel , Ġwhich Ġis Ġcurrently Ġthe Ġdeep Ġlearning Ġstate - of - the - art Ġapproach Ġfor Ġsequence Ġtagging . </s> English Ġpunct uation Ġrestoration </s> Most Ġlanguages Ġuse Ġa Ġsymbol Ġthat Ġfunctions Ġas Ġa Ġsentence Ġboundary ; Ġhowever , Ġa Ġfew Ġdo Ġnot Ġuse Ġsentence Ġmarkers Ġincluding ĠThai , ĠLa o Ġand ĠMyanmar . ĠThus , Ġfew Ġstudies Ġhave Ġinvestigated Ġsentence Ġsegment ation Ġin Ġraw Ġtext . ĠHowever , Ġstudies Ġon Ġsentence Ġsegment ation , Ġwhich Ġis Ġsometimes Ġcalled Ġsentence Ġboundary Ġdetection , Ġare Ġstill Ġfound Ġin Ġthe Ġspeech Ġrecognition Ġfield ĠB IB REF 25 Ġ. ĠThe Ġtypical Ġinput Ġto Ġspeech Ġrecognition Ġmodel Ġis Ġsimply Ġa Ġstream Ġof Ġwords . ĠIf Ġtwo Ġsentences Ġare Ġspoken Ġback Ġto Ġback , Ġby Ġdefault , Ġa Ġrecognition Ġengine Ġwill Ġtreat Ġit Ġas Ġone Ġsentence . ĠThus , Ġsentence Ġboundary Ġdetection Ġis Ġalso Ġconsidered Ġa Ġpunct uation Ġrestoration Ġtask Ġin Ġspeech Ġrecognition Ġbecause Ġwhen Ġthe Ġmodel Ġattempts Ġto Ġrestores Ġthe Ġperiod Ġin Ġthe Ġtext , Ġthe Ġsentence Ġboundary Ġposition Ġwill Ġalso Ġbe Ġdefined . </s> P unct uation Ġrestoration Ġnot Ġonly Ġprovides Ġa Ġminimal Ġsynt actic Ġstructure Ġfor Ġnatural Ġlanguage Ġprocessing , Ġsimilar Ġto Ġsentence Ġboundary Ġdetection Ġbut Ġalso Ġdramatically Ġimproves Ġthe Ġread ability Ġof Ġtranscripts . ĠTherefore , Ġpunct uation Ġrestoration Ġhas Ġbeen Ġextensively Ġstudied . ĠMany Ġapproaches Ġhave Ġbeen Ġproposed Ġfor Ġpunct uation Ġrestoration Ġthat Ġuse Ġdifferent Ġfeatures , Ġsuch Ġas Ġaudio Ġand Ġtextual Ġfeatures . ĠMoreover , Ġpunct uation Ġrestoration Ġis Ġalso Ġconsidered Ġto Ġbe Ġa Ġdifferent Ġmachine Ġlearning Ġproblem , Ġsuch Ġas Ġword Ġsequence Ġtagging Ġand Ġmachine Ġtranslation . </s> A Ġcombination Ġof Ġaudio Ġand Ġtextual Ġfeatures Ġwere Ġutilized Ġin ĠB IB REF 26 Ġ, ĠB IB REF 27 Ġ, ĠB IB REF 28 Ġto Ġpredict Ġand Ġrestore Ġpunct uation , Ġincluding Ġpitch , Ġintensity Ġand Ġpause Ġduration , Ġbetween Ġwords . ĠWe Ġignore Ġthese Ġfeatures Ġin Ġour Ġexperiment Ġbecause Ġour Ġmain Ġtask âĢĶ Th ai Ġsentence Ġsegment ation âĢĶ Ġdoes Ġnot Ġinclude Ġaudio Ġfeatures . </s> F ocusing Ġonly Ġon Ġtextual Ġfeatures , Ġthere Ġare Ġtwo Ġmain Ġapproaches , Ġnamely , Ġword Ġsequence Ġtagging Ġand Ġmachine Ġtranslation . ĠFor Ġthe Ġmachine Ġtranslation Ġapproach , Ġpunct uation Ġis Ġtreated Ġas Ġjust Ġanother Ġtype Ġof Ġtoken Ġthat Ġneeds Ġto Ġbe Ġrecovered Ġand Ġincluded Ġin Ġthe Ġoutput . ĠThe Ġmethods Ġin ĠB IB REF 29 Ġ, ĠB IB REF 30 Ġ, ĠB IB REF 31 Ġrestore Ġpunct uation Ġby Ġtranslating Ġfrom Ġunp unct uated Ġtext Ġto Ġpunct uated Ġtext . ĠHowever , Ġour Ġmain Ġtask , Ġsentence Ġsegment ation , Ġis Ġan Ġupstream Ġtask Ġin Ġtext Ġprocessing , Ġunlike Ġpunct uation Ġrestoration , Ġwhich Ġis Ġconsidered Ġa Ġdownstream Ġtask . ĠTherefore , Ġthe Ġtask Ġneeds Ġto Ġoperate Ġrapidly ; Ġconsequently , Ġwe Ġfocus Ġonly Ġon Ġthe Ġsequence Ġtagging Ġmodel , Ġwhich Ġis Ġless Ġcomplex Ġthan Ġthe Ġmachine Ġtranslation Ġmodel . </s> In Ġaddition Ġto Ġthose Ġmachine Ġtranslation Ġtasks , Ġboth Ġtraditional Ġapproaches Ġand Ġdeep Ġlearning Ġapproaches Ġmust Ġsolve Ġa Ġword Ġsequence Ġtagging Ġproblem . ĠOf Ġthe Ġtraditional Ġapproaches , Ġcontextual Ġfeatures Ġaround Ġthe Ġconsidered Ġword Ġwere Ġused Ġto Ġpredict Ġfollowing Ġpunct uation Ġin Ġthe Ġn - gram ĠB IB REF 32 Ġand ĠCR F Ġmodel Ġapproaches ĠB IB REF 33 Ġ, ĠB IB REF 34 Ġ. ĠMeanwhile , Ġin Ġthe Ġdeep Ġlearning Ġapproaches , Ġa Ġdeep Ġconv olution al Ġneural Ġnetwork ĠB IB REF 35 Ġ, ĠT - L ST M Ġ( Text ual - L ST M ) ĠB IB REF 26 Ġand Ġa Ġbid irection al ĠL ST M Ġmodel Ġwith Ġan Ġattention Ġmechanism , Ġcalled ĠT - BR NN ĠB IB REF 36 Ġ, Ġhave Ġbeen Ġadopted Ġto Ġpredict Ġa Ġpunct uation Ġsequence Ġfrom Ġthe Ġword Ġsequence . ĠT - BR NN ĠB IB REF 36 Ġwas Ġproposed Ġto Ġsolve Ġthe Ġtask Ġas Ġa Ġword - sequence Ġtagging Ġproblem , Ġand Ġit Ġis Ġcurrently Ġthe Ġbest Ġmodel Ġthat Ġuses Ġthe Ġword Ġsequence Ġtagging Ġapproach . ĠTil k ĠO . Ġet Ġal . ĠB IB REF 36 Ġalso Ġproposed Ġa Ġvariant Ġnamed ĠT - BR NN - pre , Ġwhich Ġintegrates Ġpret rained Ġword Ġvectors Ġto Ġimprove Ġthe Ġaccuracy . </s> To Ġdemonstrate Ġthat Ġour Ġmodel Ġis Ġgeneral izable Ġto Ġother Ġlanguages , Ġwe Ġcompare Ġit Ġwith Ġother Ġpunct uation Ġrestoration Ġmodels , Ġincluding ĠT - L ST M , ĠT - BR NN , Ġand ĠT - BR NN - pre . ĠThese Ġmodels Ġadopt Ġa Ġword Ġsequence Ġtagging Ġapproach Ġand Ġdo Ġnot Ġutilize Ġany Ġpros odic Ġor Ġaudio Ġfeatures . </s> Cross - View ĠTraining </s> CV T ĠB IB REF 20 Ġis Ġa Ġsemi - super vised Ġlearning Ġtechnique Ġwhose Ġgoal Ġis Ġto Ġimprove Ġthe Ġmodel Ġrepresentation Ġusing Ġa Ġcombination Ġof Ġlabeled Ġand Ġunl abel ed Ġdata . ĠDuring Ġtraining , Ġthe Ġmodel Ġis Ġtrained Ġaltern ately Ġwith Ġone Ġmini - batch Ġof Ġlabeled Ġdata Ġand ĠIN LINE FORM 0 Ġmini - bat ches Ġof Ġunl abel ed Ġdata . </s> Label ed Ġdata Ġare Ġinput Ġinto Ġthe Ġmodel Ġto Ġcalculate Ġthe Ġstandard Ġsupervised Ġloss Ġfor Ġeach Ġmini - batch Ġand Ġthe Ġmodel Ġweights Ġare Ġupdated Ġregularly . ĠMeanwhile , Ġeach Ġmini - batch Ġof Ġunl abel ed Ġdata Ġis Ġselected Ġrandomly Ġfrom Ġthe Ġpool Ġof Ġall Ġunl abel ed Ġdata ; Ġthe Ġmodel Ġcomp utes Ġthe Ġloss Ġfor ĠCV T Ġfrom Ġthe Ġmini - batch Ġof Ġunl abel ed Ġdata . ĠThis ĠCV T Ġloss Ġis Ġused Ġto Ġtrain Ġauxiliary Ġprediction Ġmodules , Ġwhich Ġsee Ġrestricted Ġviews Ġof Ġthe Ġinput , Ġto Ġmatch Ġthe Ġoutput Ġof Ġthe Ġprimary Ġprediction Ġmodule , Ġwhich Ġis Ġthe Ġfull Ġmodel Ġthat Ġsees Ġall Ġthe Ġinput . ĠMeanwhile , Ġthe Ġauxiliary Ġprediction Ġmodules Ġshare Ġthe Ġsame Ġintermediate Ġrepresentation Ġwith Ġthe Ġprimary Ġprediction Ġmodule . ĠHence , Ġthe Ġintermediate Ġrepresentation Ġof Ġthe Ġmodel Ġis Ġimproved Ġthrough Ġthis Ġprocess . </s> Similar Ġto Ġthe Ġprevious Ġwork , Ġwe Ġalso Ġapply ĠCV T Ġto Ġa Ġsequence Ġtagging Ġtask . ĠHowever , Ġour Ġmodel Ġis Ġcomposed Ġof Ġself - att ention Ġand ĠCR F Ġmodules , Ġwhich Ġwere Ġnot Ġincluded Ġin Ġthe Ġsequence Ġtagging Ġmodel Ġin ĠB IB REF 20 Ġ. ĠThe Ġprevious ĠCV T Ġwas Ġconducted Ġon Ġan ĠL ST M Ġusing Ġthe Ġconcepts Ġof Ġforward Ġand Ġbackward Ġpaths , Ġwhich Ġare Ġnot Ġintuitive ly Ġacquired Ġby Ġthe Ġself - att ention Ġmodel . ĠMoreover , Ġthe Ġoutput Ġused Ġto Ġcalculate ĠCV T Ġloss Ġwas Ġgenerated Ġby Ġthe Ġsoft max Ġfunction , Ġwhich Ġdoes Ġnot Ġoperate Ġwith ĠCR F . ĠThus , Ġin Ġour Ġstudy , Ġboth Ġthe Ġprimary Ġand Ġauxiliary Ġprediction Ġmodules Ġneeded Ġto Ġbe Ġconstructed Ġdifferently Ġfrom Ġthe Ġoriginal Ġones . </s> arch itect ure / Sem i </s> As Ġdiscussed Ġin ĠSection ĠSEC REF 3 Ġ, ĠCV T Ġrequires Ġprimary Ġand Ġauxiliary Ġprediction Ġmodules Ġfor Ġtraining Ġwith Ġunl abel ed Ġdata Ġto Ġimprove Ġthe Ġrepresentation . ĠThus , Ġwe Ġconstruct Ġboth Ġtypes Ġof Ġprediction Ġmodules Ġfor Ġour Ġmodel . ĠThe Ġflow Ġof Ġunl abel ed Ġdata , Ġwhich Ġis Ġprocessed Ġto Ġobtain Ġa Ġprediction Ġby Ġeach Ġmodule , Ġis Ġshown Ġin ĠFig . Ġ. ĠThe Ġoutput Ġof Ġeach Ġprediction Ġmodule Ġis Ġtransformed Ġinto Ġthe Ġprobability Ġdistribution Ġof Ġeach Ġclass Ġby Ġthe Ġsoft max Ġfunction Ġand Ġthen Ġused Ġto Ġcalculate ĠIN LINE FORM 0 Ġ, Ġas Ġshown Ġin Ġc vt loss . ĠDIS PLAY FORM 0 </s> The ĠIN LINE FORM 0 Ġvalue Ġis Ġbased Ġon Ġthe ĠK ull back âĢĵ Le ib ler Ġdivergence Ġ( K L Ġdivergence ) Ġbetween Ġthe Ġprobability Ġdistribution Ġof Ġthe Ġprimary ĠIN LINE FORM 1 Ġoutput Ġand Ġthose Ġof Ġtwo Ġauxiliary Ġmodules , ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġ, Ġwhere ĠIN LINE FORM 4 Ġ. ĠThe ĠKL Ġdivergence Ġat Ġeach Ġtim est ep Ġis Ġaveraged Ġwhen Ġthe Ġtim est eps Ġare Ġdropped Ġtim est eps ĠD , Ġwhich Ġis Ġdescribed Ġin ĠSection ĠUID 25 Ġ. ĠThe Ġdetails Ġof Ġthe Ġprimary Ġand Ġauxiliary Ġprediction Ġmodules , Ġwhich Ġare Ġused Ġin Ġthe ĠIN LINE FORM 5 Ġcalculation , Ġare Ġdescribed Ġin Ġthe Ġfollowing Ġsubsections . </s> In ĠB IB REF 20 Ġ, Ġthe Ġoutput Ġof Ġthe Ġprimary Ġprediction Ġmodule Ġis Ġacquired Ġfrom Ġthe Ġlast Ġlayer Ġand Ġused Ġto Ġpredict Ġtags . ĠHowever , Ġour Ġmodel Ġuses Ġa ĠCR F Ġlayer Ġto Ġdecode Ġthe Ġtags , Ġinstead Ġof Ġthe Ġsoft max Ġfunction , Ġwhose Ġinput Ġis Ġthe Ġoutput Ġfrom Ġthe Ġlast Ġfully Ġconnected Ġlayer . ĠThus , Ġthe Ġprobability Ġdistribution Ġof Ġa Ġprimary Ġprediction Ġmodule Ġshould Ġbe Ġthe Ġmarginal Ġprobability Ġacquired Ġfrom Ġthe ĠCR F Ġlayer . ĠNevertheless , Ġthe Ġforward - back ward Ġalgorithm Ġfor Ġthe Ġmarginal Ġprobability Ġcalculation Ġis Ġtime - consuming Ġwith Ġa Ġtime Ġcomplexity Ġis ĠIN LINE FORM 0 Ġ, Ġwhere ĠIN LINE FORM 1 Ġis Ġthe Ġsequence Ġlength , Ġand ĠIN LINE FORM 2 Ġis Ġthe Ġnumber Ġof Ġtags . ĠTo Ġreduce Ġthe Ġtraining Ġtime , Ġthe Ġprobability Ġdistribution Ġof Ġthe Ġprimary Ġprediction Ġmodule ĠIN LINE FORM 3 Ġis Ġinstead Ġobtained Ġfrom Ġthe Ġoutput Ġof Ġthe ĠSoft max Ġfunction , Ġwhose Ġinput Ġis Ġa Ġvirtual Ġlog it Ġvector ĠIN LINE FORM 4 Ġ, Ġas Ġshown Ġin Ġprimary . ĠDIS PLAY FORM 0 </s> Two Ġauxiliary Ġviews Ġare Ġincluded Ġto Ġimprove Ġthe Ġmodel . ĠThe Ġfirst Ġview Ġis Ġgenerated Ġfrom Ġa Ġrecurrent Ġrepresentation Ġvector ĠIN LINE FORM 0 Ġto Ġacquire Ġthe Ġlocal Ġprobability Ġdistribution ĠIN LINE FORM 1 Ġ, Ġwhere ĠIN LINE FORM 2 Ġ. ĠThe Ġsecond Ġview Ġis Ġgenerated Ġfrom Ġthe Ġlow - level Ġdistant Ġrepresentation Ġvectors ĠIN LINE FORM 3 Ġto Ġacquire Ġthe Ġprobability Ġdistribution Ġof Ġa Ġdistant Ġstructure Ġin Ġthe Ġlow - level Ġmodule ĠIN LINE FORM 4 Ġ, Ġwhere ĠIN LINE FORM 5 Ġ. ĠBy Ġgenerating Ġthe Ġviews Ġfrom Ġthese Ġrepresentation Ġvectors Ġseparately , Ġthe Ġlocal Ġand Ġdistant Ġstructures Ġin Ġthe Ġlow - level Ġmodule Ġcan Ġimprove Ġequally . </s> Although Ġboth Ġrepresentation Ġvectors Ġare Ġused Ġseparately Ġto Ġcreate Ġauxiliary Ġviews , Ġthe Ġinput Ġof Ġeach Ġstructure Ġis Ġstill Ġnot Ġrestricted , Ġunlike ĠB IB REF 20 Ġ, Ġwhere Ġthe Ġinput Ġis Ġrestricted Ġto Ġonly Ġprevious Ġor Ġfuture Ġtokens . ĠBecause ĠB ERT , Ġwhich Ġis Ġtrained Ġby Ġthe Ġmasked Ġlanguage Ġmodel , Ġoutper forms ĠOpen AI ĠG PT , Ġwhich Ġuses Ġan Ġaut ore gressive Ġapproach Ġfor Ġtraining Ġas Ġreported Ġin ĠB IB REF 14 Ġ, Ġwe Ġadopt Ġthe Ġconcept Ġof Ġthe Ġmasked Ġlanguage Ġmodel ĠB IB REF 37 Ġto Ġobtain Ġboth Ġauxiliary Ġviews . ĠThis Ġapproach Ġallows Ġthe Ġrepresentation Ġto Ġfuse Ġthe Ġleft Ġand Ġthe Ġright Ġcontext , Ġwhich Ġresults Ġin Ġa Ġbetter Ġrepresentation . ĠBy Ġusing Ġthe Ġmasked Ġlanguage Ġmodel , Ġsome Ġtokens Ġat Ġeach Ġtim est ep Ġare Ġrandomly Ġdropped Ġand Ġden oted Ġas Ġremoved Ġtokens ĠIN LINE FORM 0 Ġ; Ġthen , Ġthe Ġremaining Ġtokens Ġare Ġused Ġto Ġobtain Ġauxiliary Ġpredictions Ġin Ġthe Ġdropped Ġtim est eps ĠIN LINE FORM 1 Ġ, Ġas Ġshown Ġin ĠFig . Ġ. ĠThe Ġdetails Ġof Ġboth Ġauxiliary Ġprediction Ġmodules Ġare Ġdescribed Ġbelow . </s> arch itect ure / Mask language </s> For Ġrecurrent Ġrepresentation Ġvectors , Ġif Ġone Ġof Ġthe Ġtokens Ġis Ġdropped , Ġthe Ġrelated Ġn - gram Ġtokens Ġthat Ġinclude Ġthe Ġdropped Ġtokens Ġwill Ġalso Ġbe Ġdropped . ĠFor Ġexample , Ġif ĠIN LINE FORM 0 Ġis Ġdropped , ĠIN LINE FORM 1 Ġand ĠIN LINE FORM 2 Ġwill Ġalso Ġbe Ġdropped Ġas Ġremoved Ġtokens Ġin Ġthe Ġcase Ġof Ġa Ġbig ram . ĠThe Ġremaining Ġn - gram Ġtokens Ġare Ġthen Ġused Ġto Ġobtain Ġthe Ġrecurrent Ġrepresentation Ġvectors Ġat Ġthe Ġdropped Ġtim est eps . ĠThen , Ġthe Ġvectors Ġare Ġprovided Ġas Ġan Ġinput Ġto Ġthe Ġsoft max Ġfunction Ġto Ġobtain Ġthe Ġprobability Ġdistribution Ġof Ġthe Ġfirst Ġauxiliary Ġprediction Ġmodule , Ġas Ġshown Ġin Ġaux local . ĠDIS PLAY FORM 0 </s> In Ġthe Ġother Ġauxiliary Ġprediction Ġmodule , Ġa Ġsequence Ġof Ġthe Ġlow - level Ġdistant Ġrepresentation Ġvectors Ġis Ġgenerated Ġand Ġsome Ġtokens Ġare Ġdropped . ĠThis Ġsequence Ġof Ġvectors Ġis Ġalso Ġinput Ġinto Ġthe ĠSoft max Ġfunction , Ġjust Ġas Ġin Ġthe Ġfirst Ġauxiliary Ġprediction Ġmodule , Ġand Ġthe Ġoutput Ġis Ġanother Ġprobability Ġdistribution , Ġwhich Ġis Ġthe Ġsecond Ġauxiliary Ġprediction , Ġas Ġshown Ġin Ġaux d istant . ĠDIS PLAY FORM 0 </s> Prop osed Ġmethod </s> In Ġthis Ġsection , Ġwe Ġdescribe Ġour Ġproposed Ġmethod Ġin Ġtwo Ġsubsections . ĠThe Ġfirst Ġsubsection Ġspecifies Ġthe Ġmodel Ġarchitecture Ġand Ġthe Ġdetails Ġof Ġeach Ġmodule . ĠOur Ġfirst Ġand Ġsecond Ġcontributions , Ġwhich Ġare Ġlocal Ġand Ġdistant Ġrepresentations , Ġare Ġmainly Ġdescribed Ġin Ġthis Ġsubsection . ĠMeanwhile , Ġthe Ġsecond Ġsubsection Ġexp ounds Ġon Ġhow Ġthe Ġmodel Ġis Ġtrained Ġwith Ġunl abel ed Ġdata Ġthrough Ġthe Ġmodified ĠCV T , Ġwhich Ġis Ġour Ġthird Ġcontribution . </s> Model Ġarchitecture </s> In Ġthis Ġwork , Ġthe Ġmodel Ġpredicts Ġthe Ġtags ĠIN LINE FORM 0 Ġfor Ġthe Ġtokens Ġin Ġa Ġword Ġsequence ĠIN LINE FORM 1 Ġwhere ĠIN LINE FORM 2 Ġis Ġthe Ġsequence Ġsize Ġand ĠIN LINE FORM 3 Ġ, ĠIN LINE FORM 4 Ġdenote Ġthe Ġtoken Ġand Ġits Ġtag Ġat Ġtim est ep ĠIN LINE FORM 5 Ġ, Ġrespectively . ĠEach Ġtoken ĠIN LINE FORM 6 Ġconsists Ġof Ġa Ġword , Ġits ĠPOS Ġand Ġits Ġtype . ĠThere Ġare Ġfive Ġdefined Ġword Ġtypes : ĠEnglish , ĠThai , Ġpunct uation , Ġdigits , Ġand Ġspaces . </s> The Ġtag Ġset ĠIN LINE FORM 0 Ġis Ġpopulated Ġbased Ġon Ġthe Ġconsidered Ġtask . ĠIn ĠThai Ġsentence Ġsegment ation , Ġthe Ġassigned Ġtags Ġare ĠIN LINE FORM 1 Ġand ĠIN LINE FORM 2 Ġ; ĠIN LINE FORM 3 Ġdenotes Ġthat Ġthe Ġcorresponding Ġword Ġis Ġa Ġsentence Ġboundary Ġconsidered Ġas Ġthe Ġbeginning Ġof Ġa Ġsentence , Ġwhile Ġand ĠIN LINE FORM 4 Ġdenotes Ġthat Ġthe Ġword Ġis Ġnot Ġa Ġsentence Ġboundary . ĠMeanwhile , Ġthere Ġare Ġfour Ġtags Ġin Ġthe Ġpunct uation Ġrestoration Ġtask . ĠWords Ġnot Ġfollowed Ġby Ġany Ġpunct uation Ġare Ġtagged Ġwith ĠIN LINE FORM 5 Ġ. ĠWords Ġthat Ġare Ġfollowed Ġby Ġa Ġperiod ĠâĢ ľ . âĢ Ŀ , Ġcomma ĠâĢ ľ , âĢ Ŀ Ġor Ġquestion Ġmark ĠâĢ ľ ? âĢ Ŀ Ġare Ġtagged Ġto ĠIN LINE FORM 6 Ġ, ĠIN LINE FORM 7 Ġ, Ġand ĠIN LINE FORM 8 Ġ, Ġrespectively . </s> arch itect ure / Main Arch itect ure </s> Our Ġmodel Ġarchitecture Ġis Ġbased Ġon ĠBi - L ST M - CR F , Ġas Ġshown Ġin ĠFig . Ġ. ĠThe Ġmodel Ġis Ġdivided Ġinto Ġthree Ġmodules . ĠThe Ġfirst , Ġlow - level Ġmodule , Ġconsists Ġof Ġtwo Ġseparate Ġstructures : Ġlocal Ġand Ġdistant Ġstructures . ĠThe Ġsecond , Ġhigh - level Ġmodule , Ġcontains Ġa Ġsequence Ġof Ġstacked Ġbid irection al ĠL ST M Ġand Ġself - att ention Ġlayers . ĠThe Ġfinal Ġmodule , Ġthe Ġprediction Ġmodule , Ġis Ġresponsible Ġfor Ġpredicting Ġthe Ġtags ĠIN LINE FORM 0 Ġ. ĠEach Ġmodule Ġis Ġdescribed Ġmore Ġcompletely Ġin Ġthe Ġnext Ġthree Ġsubsections . </s> A Ġsequence Ġof Ġword Ġtokens Ġis Ġinput Ġinto Ġthe Ġlow - level Ġmodule . ĠThe Ġinput Ġtokens Ġpass Ġthrough Ġtwo Ġstructures . ĠThe Ġfirst Ġstructure Ġgenerates Ġa Ġsequence Ġof Ġlocal Ġrepresentation Ġvectors ĠIN LINE FORM 0 Ġ, Ġand Ġthe Ġsecond Ġstructure Ġgenerates Ġlow - level Ġdistant Ġrepresentation Ġvectors ĠIN LINE FORM 1 Ġ. ĠAfter Ġobtaining Ġboth Ġsequences Ġof Ġrepresentation Ġvectors , Ġthe Ġlocal Ġrepresentation Ġvectors Ġare Ġfed Ġto Ġthe ĠBi - L ST M Ġto Ġobtain Ġthe Ġrecurrent Ġrepresentation Ġvectors ĠIN LINE FORM 2 Ġ, Ġas Ġshown Ġin Ġrecurrent rep . ĠThen , Ġthe Ġrecurrent Ġand Ġdistant Ġrepresentation Ġvectors Ġare Ġconc aten ated Ġto Ġform Ġthe Ġlow - level Ġrepresentation Ġvector ĠIN LINE FORM 3 Ġ, Ġas Ġshown Ġin Ġlow - level : ĠDIS PLAY FORM 0 ĠDIS PLAY FORM 1 </s> This Ġstructure Ġis Ġshown Ġas Ġthe Ġleft Ġsub module Ġof Ġthe Ġlow - level Ġmodule Ġin ĠFig . Ġ. ĠIt Ġextracts Ġthe Ġlocal Ġrepresentation Ġvectors ĠIN LINE FORM 0 Ġ. ĠIts Ġinput Ġtokens Ġare Ġused Ġto Ġcreate Ġn - gram Ġtokens , Ġwhich Ġare Ġun ig rams ĠIN LINE FORM 1 Ġ, Ġbig rams ĠIN LINE FORM 2 Ġ, Ġand Ġtrig rams ĠIN LINE FORM 3 Ġ. ĠEach Ġn - gram Ġtoken Ġis Ġrepresented Ġas Ġan Ġembed ding Ġvector , Ġwhich Ġis Ġclassified Ġas Ġa Ġun ig ram Ġembed ding Ġvector ĠIN LINE FORM 4 Ġ, Ġa Ġbig ram Ġembed ding Ġvector ĠIN LINE FORM 5 Ġor Ġa Ġtrig ram Ġembed ding Ġvector ĠIN LINE FORM 6 Ġ. ĠEach Ġvector ĠIN LINE FORM 7 Ġis Ġmapped Ġfrom Ġa Ġtoken Ġby Ġgram Ġembed ding ĠIN LINE FORM 8 Ġ, Ġwhich Ġis Ġa Ġconc aten ated Ġvector Ġof Ġthe Ġword Ġembed ding ĠIN LINE FORM 9 Ġ, ĠPOS Ġembed ding ĠIN LINE FORM 10 Ġand Ġtype Ġembed ding ĠIN LINE FORM 11 Ġ, Ġas Ġshown Ġin Ġeq : embed ding : ĠDIS PLAY FORM 0 </s> Each Ġn - gram Ġtoken Ġat Ġtim est ep ĠIN LINE FORM 0 Ġis Ġgenerated Ġby Ġthe Ġprevious , Ġpresent Ġand Ġnext Ġtoken Ġand Ġembedded Ġinto Ġvectors Ġas Ġshown Ġin Ġun i , bi , tri . ĠThe Ġun ig ram Ġembed ding Ġat Ġtim est ep ĠIN LINE FORM 1 Ġis Ġa Ġun ig ram Ġembed ding Ġof Ġthe Ġcurrent Ġtoken ĠIN LINE FORM 2 Ġ. ĠThe Ġbig ram Ġembed ding Ġvector Ġat Ġtim est ep ĠIN LINE FORM 3 Ġis Ġa Ġbig ram Ġembed ding Ġof Ġthe Ġprevious Ġand Ġpresent Ġtokens ĠIN LINE FORM 4 Ġ, Ġand Ġthe Ġtrig ram Ġembed ding Ġvector Ġat Ġtim est ep ĠIN LINE FORM 5 Ġis Ġa Ġtrig ram Ġembed ding Ġof Ġthe Ġprevious , Ġpresent Ġand Ġnext Ġtokens ĠIN LINE FORM 6 Ġ: ĠDIS PLAY FORM 0 ĠDIS PLAY FORM 1 </s> At Ġeach Ġtim est ep ĠIN LINE FORM 0 Ġ, Ġa Ġlocal Ġrepresentation Ġvector ĠIN LINE FORM 1 Ġis Ġcombined Ġfrom Ġthe Ġn - gram Ġembed ding Ġvectors Ġgenerated Ġfrom Ġthe Ġcontext Ġaround ĠIN LINE FORM 2 Ġ. ĠA Ġcombination Ġof Ġembed ding Ġvectors , Ġwhich Ġis Ġused Ġto Ġconstruct Ġa Ġlocal Ġrepresentation Ġvector , Ġis Ġshown Ġin Ġn - gram comb ination . ĠA Ġcombination Ġconsists Ġof Ġthe Ġun ig ram , Ġbig ram , Ġand Ġtrig ram Ġembed ding Ġvectors Ġat Ġtim est eps ĠIN LINE FORM 3 Ġ, ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġand Ġit Ġis Ġa Ġconc aten ation Ġof Ġall Ġthe Ġembed ding Ġvectors : ĠDIS PLAY FORM 0 </s> The Ġdistant Ġstructure , Ġwhich Ġis Ġa Ġself - att ention Ġmodule , Ġis Ġshown Ġin ĠFig . Ġon Ġthe Ġright Ġside Ġof Ġthe Ġlow - level Ġmodule . ĠThe Ġstructure Ġextracts Ġlow - level Ġdistant Ġrepresentation Ġvectors ĠIN LINE FORM 0 Ġfrom Ġa Ġsequence Ġof Ġun ig ram Ġembed ding Ġvectors ĠIN LINE FORM 1 Ġ, Ġas Ġshown Ġin Ġdistant att ention . ĠIn Ġthis Ġcase , Ġthe Ġself - att ention Ġmodule Ġis Ġa Ġscaled Ġdot - product Ġattention ĠB IB REF 11 Ġ, Ġwhere Ġkey , Ġquery , Ġand Ġvalue Ġvectors Ġare Ġthe Ġlinear Ġprojections Ġof Ġthe Ġun ig ram Ġembed ding Ġvectors Ġshown Ġin ĠFig . Ġ. ĠThe Ġlinear Ġtransformations Ġfor Ġkey , Ġquery , Ġand Ġvalue Ġare Ġlearned Ġseparately Ġand Ġupdated Ġin Ġthe Ġmodel Ġthrough Ġback prop ag ation . ĠThe Ġoutput Ġvector , Ġwhich Ġis Ġthe Ġscaled Ġdot - product Ġattention Ġat Ġeach Ġtim est ep , Ġis Ġconc aten ated Ġwith Ġthe Ġinput Ġvector ĠIN LINE FORM 2 Ġand Ġprojected Ġby Ġa Ġlinear Ġtransformation . ĠThat Ġprojected Ġvector Ġis Ġthe Ġoutput Ġvector Ġof Ġa Ġself - att ention Ġmodule , Ġwhich Ġis Ġa Ġlow - level Ġdistant Ġrepresentation Ġvector . </s> arch itect ure / self att ention ĠDIS PLAY FORM 0 </s> The Ġlow - level Ġrepresentation Ġvectors ĠIN LINE FORM 0 Ġare Ġused Ġas Ġthe Ġinput Ġfor Ġthis Ġmodule , Ġwhich Ġoutputs Ġthe Ġhigh - level Ġrepresentation Ġvectors ĠIN LINE FORM 1 Ġwhose Ġcalculation Ġis Ġshown Ġin Ġhigh - level . ĠThe Ġhigh - level Ġmodule , Ġas Ġshown Ġin ĠFig . Ġ, Ġis Ġcomposed Ġof Ġa Ġstacked Ġbid irection al ĠL ST M Ġand Ġa Ġself - att ention Ġmodules . ĠA Ġstacked Ġbid irection al ĠL ST M Ġcontains ĠK Ġlayers Ġof Ġbid irection al ĠL ST Ms Ġin Ġwhich Ġthe Ġoutput Ġfrom Ġthe Ġprevious Ġbid irection al ĠL ST M Ġlayer Ġis Ġthe Ġinput Ġof Ġthe Ġnext Ġbid irection al ĠL ST M Ġlayer . ĠThe Ġself - att ention Ġpart Ġof Ġthis Ġstructure Ġis Ġthe Ġsame Ġas Ġthat Ġin Ġthe Ġlow - level Ġdistant Ġstructure . ĠThe Ġself - att ention Ġmodule Ġhelps Ġto Ġgenerate Ġthe Ġhigh - level Ġdistant Ġrepresentation Ġvectors Ġthat Ġare Ġoutput Ġby Ġthe Ġhigh - level Ġmodule . ĠDIS PLAY FORM 0 </s> The Ġprediction Ġmodule Ġis Ġthe Ġlast Ġmodule . ĠIt Ġincludes Ġtwo Ġlayers : Ġa Ġfully Ġconnected Ġlayer Ġand Ġa ĠCR F Ġlayer . ĠIn Ġthe Ġfully Ġconnected Ġlayer , Ġthe Ġoutput Ġvectors Ġfrom Ġthe Ġhigh - level Ġmodule Ġare Ġprojected Ġby Ġa Ġlinear Ġtransformation Ġas Ġshown Ġin Ġvirtual . ĠThe Ġpurpose Ġof Ġthis Ġlayer Ġis Ġto Ġcreate Ġthe Ġvirtual Ġlog it Ġvectors ĠIN LINE FORM 0 Ġ, Ġwhich Ġrepresent Ġthe Ġprobability Ġdistribution Ġfor ĠCV T , Ġas Ġdiscussed Ġin ĠSection ĠSEC REF 21 Ġ. ĠTherefore , Ġthe Ġnumber Ġof Ġdimensions Ġof Ġlog its Ġequals Ġthe Ġnumber Ġof Ġpossible Ġtags Ġin Ġeach Ġtask : ĠDIS PLAY FORM 0 </s> The ĠCR F Ġlayer Ġis Ġresponsible Ġfor Ġpredicting Ġthe Ġtag ĠIN LINE FORM 0 Ġof Ġa Ġtoken Ġat Ġeach Ġtim est ep , Ġas Ġshown Ġin Ġcr f . ĠThe Ġlayer Ġreceives Ġa Ġsequence Ġof Ġvirtual Ġlog it Ġvectors Ġ( ĠIN LINE FORM 1 Ġ) Ġas Ġinput Ġand Ġthen Ġdec odes Ġthem Ġto Ġa Ġsequence Ġof Ġtags ĠIN LINE FORM 2 Ġusing Ġthe ĠV iter bi Ġalgorithm . ĠDIS PLAY FORM 0 </s> Dat as ets </s> Three Ġdatasets Ġare Ġused Ġin Ġthe Ġexperiments Ġas Ġdescribed Ġin Ġthe Ġfollowing Ġsubsections . ĠWe Ġuse Ġtwo Ġdatasets Ġfor ĠThai Ġsentence Ġsegment ation , Ġand Ġthe Ġthird Ġdataset Ġis Ġused Ġfor ĠEnglish Ġpunct uation Ġrestoration . ĠThe Ġstatistics Ġof Ġthe Ġpre process ed Ġdata Ġare Ġshown Ġin ĠTable ĠT AB REF 31 Ġ, Ġincluding Ġthe Ġnumber Ġof Ġsequences Ġand Ġthe Ġnumber Ġof Ġvocabulary Ġwords Ġin Ġeach Ġdataset . ĠWe Ġalso Ġcalculate Ġthe Ġaverage Ġnumber Ġof Ġwords Ġper Ġpassage Ġin Ġthe Ġunl abel ed Ġdata Ġthat Ġdo Ġnot Ġappear Ġin Ġthe Ġlabeled Ġdata , Ġas Ġshown Ġin ĠTable ĠT AB REF 32 Ġ. </s> This Ġdataset Ġis Ġa ĠThai Ġpart - of - speech - tag ged Ġdataset Ġcontaining Ġ10 , 864 Ġsentences . ĠIn Ġthe Ġcorpus , Ġtext Ġwas Ġseparated Ġinto Ġparagraphs , Ġsentences , Ġand Ġwords Ġhierarch ically Ġby Ġlingu ists . ĠEach Ġword Ġwas Ġalso Ġmanually Ġassigned Ġa ĠPOS Ġby Ġlingu ists . ĠThese Ġdata Ġinclude Ġno Ġunl abel ed Ġdata Ġwith Ġthe Ġsame Ġword Ġsegment ation Ġand ĠPOS Ġtag Ġset . ĠHence , Ġwe Ġdo Ġnot Ġexecute ĠCV T Ġon Ġthis Ġdataset . </s> Our Ġdata Ġpre processing Ġon Ġthe ĠOR CH ID Ġcorpus Ġwas Ġsimilar Ġto Ġthat Ġin ĠB IB REF 8 Ġ: Ġall Ġthe Ġcomments Ġare Ġremoved , Ġand Ġthe Ġdata Ġare Ġpartition ed Ġinto Ġ10 Ġparts Ġcontaining Ġequal Ġnumbers Ġof Ġsentences Ġto Ġsupport Ġ10 - fold Ġcross - valid ation . ĠEach Ġtraining Ġset Ġis Ġsplit Ġinto Ġone Ġpart Ġused Ġfor Ġvalidation Ġand Ġthe Ġrest Ġis Ġused Ġfor Ġmodel Ġtraining . ĠSub sequently , Ġall Ġthe Ġwords Ġin Ġeach Ġdataset Ġare Ġconc aten ated Ġand Ġthen Ġseparated Ġinto Ġsequences Ġwith Ġ200 Ġwords Ġper Ġinstance . ĠEach Ġsequence Ġalways Ġbegins Ġwith Ġthe Ġfirst Ġword Ġof Ġa Ġsentence . ĠIf Ġa Ġsequence Ġends Ġwith Ġan Ġunfinished Ġsentence , Ġthe Ġnext Ġsequence Ġstarts Ġwith Ġthat Ġcomplete Ġsentence . </s> This ĠThai Ġdataset Ġincludes Ġmany Ġtypes Ġof Ġlabeled Ġdata Ġuseful Ġin Ġsentence Ġsegment ation Ġtasks . ĠThe Ġraw Ġtext Ġwas Ġgenerated Ġby Ġusers Ġhaving Ġconversations Ġin Ġthe Ġfinancial Ġdomain Ġand Ġwere Ġacquired Ġmainly Ġby Ġcrawling Ġsocial Ġsites . ĠThe Ġlabeled Ġdata Ġfor Ġsentence Ġsegment ation Ġwere Ġmanually Ġannot ated Ġby Ġlingu ists Ġusing Ġthe Ġdefinitions Ġin ĠB IB REF 21 Ġ. </s> At Ġthe Ġtime Ġof Ġthis Ġstudy , Ġthe Ġdataset Ġwas Ġextended Ġfrom Ġthat Ġin ĠB IB REF 21 Ġ; Ġthe Ġdata Ġwere Ġcollected Ġfrom ĠJanuary Ġ2017 Ġto ĠDecember Ġ2017 . ĠThe Ġlabeled Ġdataset Ġincludes Ġ48 , 374 Ġpassages . ĠTo Ġsupport Ġsemi - super vised Ġlearning , Ġthe Ġfirst Ġ3 Ġmonths Ġof Ġdata Ġ( 96 , 777 Ġpassages ) Ġare Ġunl abel ed . </s> Because Ġthe Ġdata Ġstem Ġfrom Ġsocial Ġmedia , Ġsome Ġtext Ġexists Ġthat Ġcannot Ġbe Ġconsidered Ġas Ġpart Ġof Ġany Ġsentence , Ġsuch Ġas Ġproduct Ġlinks , Ġsymbols Ġunrelated Ġto Ġsentences , Ġand Ġspace Ġbetween Ġsentences . ĠThese Ġportions Ġwere Ġnot Ġoriginally Ġannot ated Ġas Ġsentences Ġby Ġthe Ġlingu ists . ĠHowever , Ġin Ġthis Ġwork , Ġwe Ġtreat Ġthese Ġportions Ġas Ġindividual Ġsentences Ġand Ġtag Ġthe Ġfirst Ġword Ġof Ġeach Ġfraction Ġas Ġthe Ġsentence Ġboundary . </s> For Ġevaluation Ġpurposes , Ġthe Ġcollection Ġof Ġpassages Ġin Ġthis Ġdataset Ġis Ġbased Ġon Ġ5 - fold Ġcross - valid ation , Ġsimilar Ġto Ġthe Ġprevious Ġwork ĠB IB REF 21 Ġ. ĠThe Ġpassages Ġare Ġtreated Ġas Ġinput Ġsequences Ġfor Ġthe Ġmodel . ĠFor Ġeach Ġpassage , Ġword Ġsegment ation Ġand ĠPOS Ġtagging Ġare Ġprocessed Ġby Ġthe Ġcustom Ġmodels Ġfrom Ġthis Ġdataset . </s> We Ġadopted Ġthis ĠEnglish - language Ġdataset Ġto Ġenable Ġcomparisons Ġwith Ġmodels Ġintended Ġfor Ġother Ġlanguages . ĠThe Ġdataset Ġis Ġcomposed Ġof ĠTED Ġtalk Ġtranscripts . ĠTo Ġcompare Ġour Ġmodel Ġwith Ġthose Ġof Ġprevious Ġworks , Ġwe Ġselected Ġthe Ġtraining Ġdataset Ġfor Ġthe Ġmachine Ġtranslation Ġtrack Ġin ĠI W SL T 2012 Ġand Ġseparated Ġit Ġinto Ġtraining Ġand Ġvalidation Ġsets Ġcontaining Ġ2 . 1 Ġmillion Ġand Ġ295 Ġthousand Ġwords , Ġrespectively . ĠThe Ġtesting Ġdataset Ġis Ġthe ĠI W SL T 2011 Ġreference Ġset , Ġwhich Ġcontains Ġ13 Ġthousand Ġwords . ĠTo Ġacquire Ġunl abel ed Ġdata Ġfor Ġsemi - super vised Ġlearning , Ġwe Ġadopted Ġthe ĠI W SL T 2016 Ġmachine Ġtranslation Ġtrack Ġtraining Ġdata ; Ġduplicate Ġtalks Ġthat Ġalso Ġappear Ġin ĠI W SL T 2012 Ġare Ġdiscarded . </s> The Ġdata Ġpre processing Ġfollows Ġthe Ġprocess Ġin ĠB IB REF 36 Ġ. ĠEach Ġsequence Ġis Ġgenerated Ġfrom Ġ200 Ġwords , Ġof Ġwhich Ġbeginning Ġis Ġalways Ġthe Ġfirst Ġword Ġin Ġa Ġsentence . ĠIf Ġa Ġsentence Ġis Ġcut Ġat Ġthe Ġend Ġof Ġa Ġsequence , Ġthat Ġsentence Ġis Ġcopied Ġin Ġfull Ġto Ġthe Ġbeginning Ġof Ġthe Ġnext Ġsequence . </s> To Ġuse Ġour Ġmodel , Ġthe ĠPOS Ġof Ġeach Ġword Ġis Ġrequired . ĠHowever , Ġthe ĠI W SL T Ġdataset Ġcontains Ġonly Ġthe Ġraw Ġtext Ġof Ġtranscripts Ġand Ġdoes Ġnot Ġinclude ĠPOS Ġtags . ĠThus , Ġwe Ġimplement ĠPOS Ġtagging Ġusing Ġa Ġspecial Ġlibrary ĠB IB REF 38 Ġto Ġpredict Ġthe ĠPOS Ġof Ġeach Ġword . </s> Im plementation ĠDetail </s> Before Ġmapping Ġeach Ġtoken Ġincluded Ġin Ġthe Ġun ig ram , Ġbig ram , Ġand Ġtrig ram Ġto Ġthe Ġembed ding Ġvector , Ġwe Ġlimit Ġthe Ġminimum Ġfrequency Ġof Ġoccurring Ġwords Ġthat Ġare Ġnot Ġmarked Ġas Ġan Ġunknown Ġtoken . ĠThere Ġare Ġ2 Ġparameters Ġset Ġfor Ġthe Ġun ig ram ĠIN LINE FORM 0 Ġand Ġthe Ġremaining ĠIN LINE FORM 1 Ġ, Ġrespectively . ĠWe Ġfound Ġthat Ġmodel Ġaccuracy Ġis Ġhighly Ġsensitive Ġto Ġthese Ġparameters . ĠTherefore , Ġwe Ġuse Ġa Ġgrid Ġsearch Ġtechnique Ġto Ġfind Ġthe Ġbest Ġvalue Ġfor Ġboth Ġparameters Ġfor Ġthe Ġmodel . </s> We Ġapply Ġtwo Ġoptim izers Ġused Ġin Ġthis Ġwork : ĠAd ag ard ĠB IB REF 39 Ġand ĠAdam ĠB IB REF 40 Ġ, Ġwhose Ġlearning Ġrates Ġare Ġset Ġto Ġ0 . 02 Ġand Ġ0 . 001 Ġfor Ġthe ĠThai Ġand ĠEnglish Ġdatasets , Ġrespectively . ĠTo Ġgeneral ize Ġthe Ġmodel , Ġwe Ġalso Ġintegrate ĠL 2 Ġregular ization Ġwith Ġan Ġalpha Ġof Ġ0 . 01 Ġto Ġthe Ġloss Ġfunction Ġfor Ġmodel Ġupdating . ĠMoreover , Ġdrop out Ġis Ġapplied Ġto Ġthe Ġlocal Ġrepresentation Ġvectors , Ġrecurrent Ġrepresentation Ġvectors , Ġbetween Ġall Ġbid irection al ĠL ST Ms Ġand Ġenclosed Ġby Ġthe Ġself - att ention Ġmechanism Ġin Ġthe Ġhigh - level Ġmodule . </s> During Ġtraining , Ġboth Ġthe Ġsupervised Ġand Ġsemi - super vised Ġmodels Ġare Ġtrained Ġuntil Ġthe Ġvalidation Ġmetrics Ġstop Ġimproving ; Ġthe Ġmetrics Ġare Ġ( 1 ) Ġsentence Ġboundary ĠF 1 Ġscore Ġand Ġ( 2 ) Ġoverall ĠF 1 Ġscore Ġfor ĠThai Ġsentence Ġsegment ation Ġand ĠEnglish Ġpunct uation Ġrestoration , Ġrespectively . </s> CV T Ġhas Ġthree Ġmain Ġparameters Ġthat Ġimpact Ġmodel Ġaccuracy . ĠThe Ġfirst Ġis Ġthe Ġdrop Ġrate Ġof Ġthe Ġmasked Ġlanguage Ġmodel , Ġwhich Ġdetermines Ġthe Ġnumber Ġof Ġtokens Ġthat Ġare Ġdropped Ġand Ġused Ġfor Ġlearning Ġauxiliary Ġprediction Ġmodules Ġas Ġdescribed Ġin ĠSection ĠSEC REF 21 Ġ. ĠThe Ġsecond Ġis Ġthe Ġnumber Ġof Ġunl abel ed Ġmini - bat ches ĠIN LINE FORM 0 Ġused Ġfor Ġtraining Ġbetween Ġsupervised Ġmini - bat ches . ĠThird , Ġrather Ġthan Ġusing Ġthe Ġsame Ġdrop out Ġrate Ġfor Ġthe Ġlocal Ġrepresentation Ġvectors , Ġa Ġnew Ġdrop out Ġrate Ġis Ġassigned . </s> The Ġdetails Ġof Ġhyper param eters Ġsuch Ġas Ġthe Ġhidden Ġsize Ġof Ġeach Ġlayer Ġand Ġdrop out Ġrate Ġare Ġgiven Ġin ĠSection ĠSEC REF 7 Ġ. </s> E val uation </s> During Ġthe Ġevaluation , Ġeach Ġtask Ġis Ġassessed Ġusing Ġdifferent Ġmetrics Ġbased Ġon Ġprevious Ġworks . ĠFor ĠThai Ġsentence Ġsegment ation , Ġthree Ġmetrics Ġare Ġused Ġin Ġthe Ġevaluation : Ġsentence Ġboundary ĠF 1 Ġscore , Ġnon - sent ence Ġboundary ĠF 1 Ġscore , Ġand Ġspace Ġcorrect ĠB IB REF 8 Ġ. ĠIn Ġthis Ġwork , Ġwe Ġmainly Ġfocus Ġon Ġthe Ġperformance Ġof Ġsentence Ġboundary Ġprediction Ġand Ġnot Ġnon - sent ence Ġboundary Ġprediction Ġor Ġspace Ġprediction . ĠTherefore , Ġwe Ġmake Ġcomparisons Ġwith Ġother Ġmodels Ġregarding Ġonly Ġtheir Ġsentence Ġboundary ĠF 1 Ġscores . ĠThe Ġequation Ġfor Ġthe Ġsentence Ġboundary ĠF 1 Ġscore Ġmetric Ġis Ġshown Ġin Ġf 1 sb . ĠIn Ġcalculating Ġthe ĠF 1 Ġscore , Ġthe Ġpositive Ġclass Ġis Ġdefined Ġas Ġthe Ġsentence Ġboundary , Ġand Ġthe Ġnegative Ġclass Ġis Ġdefined Ġas Ġthe Ġnon - sent ence Ġboundary . ĠIN LINE FORM 0 ĠIN LINE FORM 1 </s> For ĠEnglish Ġpunct uation , Ġthe Ġevaluation Ġis Ġmeasured Ġon Ġeach Ġtype Ġof Ġpunct uation Ġand Ġoverall ĠF 1 Ġscore . ĠFor Ġthe Ġpunct uation Ġrestoration Ġtask , Ġwe Ġcare Ġonly Ġabout Ġthe Ġperformance Ġof Ġthe Ġsamples Ġbelonging Ġto Ġthe Ġclasses Ġthat Ġare Ġtagged Ġto Ġwords Ġfollowed Ġby Ġpunct uation ; Ġtherefore Ġclass ĠIN LINE FORM 0 Ġ, Ġwhich Ġrepresents Ġwords Ġnot Ġimmediately Ġfollowed Ġby Ġpunct uation , Ġis Ġignored Ġin Ġthe Ġevaluation . ĠConsequently , Ġthe Ġoverall ĠF 1 Ġscore Ġdoes Ġnot Ġinclude ĠIN LINE FORM 1 Ġas Ġthe Ġpositive Ġclass Ġin Ġf 1 over all . ĠIN LINE FORM 2 ĠIN LINE FORM 3 </s> To Ġcompare Ġthe Ġperformance Ġof Ġeach Ġpunct uation Ġrestoration Ġmodel Ġin Ġa Ġmanner Ġsimilar Ġto Ġsentence Ġsegment ation , Ġthe Ġ2 - class ĠF 1 Ġscore Ġis Ġcalculated Ġto Ġmeasure Ġmodel Ġaccuracy , Ġas Ġshown Ġin Ġf 12 class . ĠThe Ġcalculation Ġof Ġthis Ġmetric Ġis Ġthe Ġsame Ġas Ġthat Ġused Ġin ĠB IB REF 35 Ġ. ĠThe Ġmetric Ġconsiders Ġonly Ġwhere Ġthe Ġpunct uation Ġposition Ġis Ġand Ġignores Ġthe Ġtype Ġof Ġrestored Ġpunct uation . ĠTherefore , Ġthis Ġmeasure Ġis Ġsimilar Ġto Ġthe Ġmetric Ġsentence Ġboundary ĠF 1 , Ġwhich Ġonly Ġconsiders Ġthe Ġposition Ġof Ġthe Ġmissing Ġpunct uation . ĠIN LINE FORM 0 ĠIN LINE FORM 1 </s> Results Ġand Ġdiscussions </s> We Ġreport Ġand Ġdiscuss Ġthe Ġresults Ġof Ġour Ġtwo Ġtasks Ġin Ġfour Ġsubsections . ĠThe Ġfirst Ġand Ġsecond Ġsubsections Ġinclude Ġthe Ġeffect Ġof Ġlocal Ġrepresentation Ġand Ġdistant Ġrepresentation , Ġrespectively . ĠThe Ġimpact Ġof ĠCV T Ġis Ġexplained Ġin Ġthe Ġthird Ġsubsection . ĠThe Ġlast Ġsubsection Ġpresents Ġa Ġcomparison Ġof Ġour Ġmodel Ġand Ġall Ġthe Ġbas elines . ĠMoreover , Ġwe Ġalso Ġconduct Ġpaired Ġt - tests Ġto Ġinvestigate Ġthe Ġsignificance Ġof Ġthe Ġimprovement Ġfrom Ġeach Ġcontribution , Ġas Ġshown Ġin ĠSection ĠSEC REF 9 Ġ. </s> Effect Ġof Ġlocal Ġrepresentation </s> To Ġfind Ġthe Ġeffect Ġof Ġlocal Ġrepresentation , Ġwe Ġcompare Ġa Ġstandard ĠBi - L ST M - CR F Ġmodel Ġusing Ġour Ġfull Ġimplementation Ġto Ġthe Ġmodel Ġthat Ġincludes Ġn - gram Ġembed ding Ġto Ġextract Ġlocal Ġrepresentation . ĠIn Ġtab : th aires ult , tab : eng result , Ġthe Ġstandard ĠBi - L ST M - CR F Ġmodel Ġis Ġrepresented Ġas ĠBi - L ST M - CR F Ġ( row Ġ( e )), Ġwhile Ġthe Ġmodels Ġwith Ġlocal Ġfeatures Ġare Ġrepresented Ġas ĠIN LINE FORM 0 Ġ( row Ġ( f )). </s> The Ġresults Ġin ĠTable ĠT AB REF 45 Ġshow Ġthat Ġusing Ġn - gram Ġto Ġobtain Ġthe Ġlocal Ġrepresentation Ġimproves Ġthe ĠF 1 Ġscore Ġof Ġthe Ġmodel Ġfrom Ġ90 . 9 % Ġ( row Ġ( e )) Ġto Ġ92 . 4 % Ġ( row Ġ( f )) Ġon Ġthe ĠOr chid Ġdataset Ġand Ġfrom Ġ87 . 6 % Ġ( row Ġ( e )) Ġto Ġ88 . 7 % Ġ( row Ġ( f )) Ġon Ġthe ĠU G WC Ġdataset . ĠThese Ġresults Ġoccur Ġbecause Ġmany Ġword Ġgroups Ġexist Ġthat Ġcan Ġbe Ġused Ġto Ġsignal Ġthe Ġbeginning Ġand Ġend Ġof Ġa Ġsentence Ġin ĠThai . ĠWord Ġgroups Ġalways Ġfound Ġnear Ġsentence Ġboundaries Ġcan Ġbe Ġcategorized Ġinto Ġ2 Ġgroups . ĠThe Ġfirst Ġgroup Ġconsists Ġof Ġfinal Ġparticles , Ġe . g ., ĠâĢ ľ th ai à¸ Ļ à¸ ° | à¸ Ħ à¸ ° ĠâĢ Ŀ Ġ( na Ġ| Ġk ha ), ĠâĢ ľ th ai à¸ Ļ à¸ ° | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( na Ġ| Ġk hr á º ¡ b ), ĠâĢ ľ th ai à¹ Ģ à¸ ¥ à¸ ¢ | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( ley Ġ| Ġk hr á º ¡ b ), ĠâĢ ľ th ai à¹ ģ à¸ ¥ à¹ ī à¸ § | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( la Ãª w Ġ| Ġk hr á º ¡ b ), Ġand Ġothers . ĠThese Ġword Ġgroups Ġare Ġusually Ġused Ġat Ġthe Ġends Ġof Ġsentences Ġto Ġindicate Ġthe Ġform ality Ġlevel . ĠFor Ġinstance , Ġthe Ġmodel Ġwith Ġlocal Ġrepresentation Ġcan Ġdetect Ġthe Ġsentence Ġboundary Ġat ĠâĢ ľ th ai à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( k hr á º ¡ b ) Ġthat Ġis Ġfollowed Ġby ĠâĢ ľ th ai à¹ ģ à¸ ¥ à¹ ī à¸ § ĠâĢ Ŀ Ġ( la Ãª w ), Ġas Ġshown Ġin ĠFig . Ġ, Ġwhile Ġthe Ġmodel Ġwithout Ġlocal Ġrepresentation Ġcannot Ġdetect Ġthe Ġword Ġas Ġa Ġsentence Ġboundary . ĠThe Ġsecond Ġgroup Ġconsists Ġof Ġconj unctions Ġthat Ġare Ġalways Ġused Ġat Ġthe Ġbeginnings Ġof Ġsentences , Ġe . g ., ĠâĢ ľ th ai à¸ Ī à¸ ² à¸ ģ | à¸ Ļ à¸ ± à¹ ī à¸ Ļ Ġ( after Ġthat ) ĠâĢ Ŀ , ĠâĢ ľ th ai à¹ Ħ à¸ ¡ à¹ Ī | à¸ ĩ à¸ ± à¹ ī à¸ Ļ Ġ( other wise ) ĠâĢ Ŀ Ġand Ġothers . ĠThe Ġmodel Ġthat Ġuses Ġn - gram Ġto Ġcapture Ġword Ġgroup Ġinformation Ġis Ġbetter Ġable Ġto Ġdetect Ġword Ġgroups Ġnear Ġsentence Ġboundaries . ĠThus , Ġthis Ġmodel Ġcan Ġidentify Ġthese Ġsentence Ġboundaries Ġeasily Ġin Ġthe ĠThai Ġlanguage . </s> result / cher ry pick ng ram </s> In Ġcontrast , Ġfor Ġthe ĠEnglish Ġdataset , Ġlocal Ġrepresentation Ġusing Ġn - gram Ġdrops Ġthe Ġoverall ĠF 1 Ġscore Ġof Ġpunct uation Ġrestoration Ġfrom Ġ64 . 4 % Ġ( row Ġ( e )) Ġto Ġ63 . 6 % Ġ( row Ġ( f )), Ġas Ġshown Ġin ĠTable ĠT AB REF 47 Ġ. ĠHowever , Ġthe Ġ2 - class ĠF 1 Ġscore Ġincreases Ġslightly Ġfrom Ġ81 . 4 % Ġ( row Ġ( e )) Ġto Ġ81 . 8 % Ġ( row Ġ( f )) Ġwhen Ġcompared Ġto Ġthe ĠBi - L ST M - CR F Ġmodel , Ġwhich Ġdoes Ġnot Ġintegrate Ġn - gram Ġembed ding . ĠCommon Ġphrases Ġsuch Ġas ĠâĢ Ŀ In Ġspite Ġof âĢ Ŀ , ĠâĢ Ŀ Even Ġthough âĢ Ŀ Ġand ĠâĢ Ŀ Due Ġto Ġthe Ġfact âĢ Ŀ Ġmight Ġprovide Ġstrong Ġcues Ġfor Ġpunct uation ; Ġhowever , Ġsuch Ġphrases Ġcan Ġbe Ġfound Ġat Ġboth Ġthe Ġbeginnings Ġand Ġin Ġthe Ġmiddle Ġof Ġsentences . ĠBecause Ġsuch Ġphrases Ġcan Ġbe Ġused Ġin Ġboth Ġpositions , Ġthey Ġmay Ġfollow Ġcomm as Ġwhen Ġthey Ġare Ġin Ġthe Ġmiddle Ġof Ġthe Ġsentence Ġor Ġperiods Ġwhen Ġthey Ġare Ġat Ġthe Ġbeginning Ġof Ġa Ġsentence . ĠHowever , Ġthey Ġstill Ġfollow Ġeither Ġa Ġperiod Ġor Ġa Ġcomma ; Ġconsequently , Ġsuch Ġphrases Ġcan Ġstill Ġhelp Ġidentify Ġwhether Ġthe Ġpunct uation Ġshould Ġbe Ġrestored , Ġwhich Ġincreases Ġthe Ġ2 - class ĠF 1 Ġscore , Ġwhich Ġconsiders Ġonly Ġthe Ġpositions Ġof Ġmissing Ġpunct uation . ĠMoreover , ĠEnglish Ġdoes Ġnot Ġuse Ġthe Ġconcept Ġof Ġa Ġfinal Ġparticle Ġusually Ġfound Ġat Ġthe Ġend Ġof Ġthe Ġsentence âĢĶ similar Ġto Ġthe ĠThai Ġword Ġgroup Ġmentioned Ġearlier âĢĶ including ĠâĢ ľ th ai à¸ Ļ à¸ ° | à¸ Ħ à¸ ° ĠâĢ Ŀ ( na Ġ| Ġk ha ), ĠâĢ ľ th ai à¸ Ļ à¸ ° | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ ( na Ġ| Ġk hr á º ¡ b ), ĠâĢ ľ th ai à¹ Ģ à¸ ¥ à¸ ¢ | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( ley | k hr á º ¡ b ), ĠâĢ ľ th ai à¹ ģ à¸ ¥ à¹ ī à¸ § | à¸ Ħ à¸ £ à¸ ± à¸ ļ ĠâĢ Ŀ Ġ( la Ãª w Ġ| Ġk hr á º ¡ b ) Ġand Ġothers . ĠTherefore , Ġthe Ġword Ġgroups Ġcaptured Ġby Ġn - gram Ġcan Ġonly Ġhelp Ġto Ġidentify Ġwhere Ġpunct uation Ġshould Ġbe Ġrestored Ġbut Ġthey Ġdo Ġnot Ġhelp Ġthe Ġmodel Ġdetermine Ġthe Ġtype Ġof Ġpunct uation Ġthat Ġshould Ġbe Ġrestored . </s> Effect Ġof Ġdistant Ġrepresentation </s> The Ġeffect Ġof Ġthis Ġcontribution Ġcan Ġbe Ġfound Ġby Ġcomparing Ġthe Ġmodel Ġthat Ġintegrates Ġthe Ġdistant Ġrepresentation Ġand Ġthe Ġmodel Ġthat Ġdoes Ġnot . ĠThe Ġmodel Ġwith Ġdistant Ġfeatures Ġintegrated Ġis Ġrepresented Ġas ĠIN LINE FORM 0 Ġ( row Ġ( g )) Ġin Ġboth Ġtables . ĠIn Ġthis Ġcase , Ġthe Ġdistant Ġrepresentation Ġis Ġcomposed Ġof Ġthe Ġself - att ention Ġmodules Ġin Ġboth Ġthe Ġlow - Ġand Ġhigh - level Ġmodules , Ġas Ġshown Ġin ĠFig . Ġ. </s> From Ġthe Ġcombination Ġof Ġlocal Ġand Ġdistant Ġrepresentation , Ġthe Ġresults Ġin Ġtab : th aires ult , tab : eng result Ġshow Ġthat Ġthe Ġdistant Ġfeature Ġimproves Ġthe Ġaccuracy Ġof Ġthe Ġmodel Ġon Ġall Ġdatasets Ġcompared Ġto Ġthe Ġmodel Ġwith Ġno Ġdistant Ġrepresentation . ĠThe ĠF 1 Ġscores Ġof Ġthe Ġsentence Ġsegment ation Ġmodels Ġimproved Ġslightly , Ġfrom Ġ92 . 4 % Ġand Ġ88 . 7 % Ġ( row Ġ( f )) Ġto Ġ92 . 5 % Ġand Ġ88 . 8 % Ġ( row Ġ( g )) Ġon Ġthe ĠOr chid Ġand ĠU G WC Ġdatasets , Ġrespectively . ĠFor Ġthe ĠI W SL T Ġdataset , Ġthe Ġdistant Ġfeature Ġcan Ġrecover Ġthe Ġoverall ĠF 1 Ġscore Ġof Ġpunct uation Ġrestoration , Ġwhich Ġis Ġdegraded Ġby Ġthe Ġn - gram Ġembed ding ; Ġit Ġimproves Ġfrom Ġ63 . 6 % Ġ( row Ġ( f )) Ġto Ġ64 . 5 % Ġ( row Ġ( g )). ĠThe Ġreason Ġis Ġthat Ġthe Ġself - att ention Ġmodules Ġfocus Ġselectively Ġon Ġcertain Ġparts Ġof Ġthe Ġpassage . ĠThus , Ġthe Ġmodel Ġfocuses Ġon Ġthe Ġinitial Ġwords Ġof Ġthe Ġdependent Ġclauses , Ġwhich Ġhelps Ġin Ġclass ifying Ġwhich Ġtype Ġof Ġpunct uation Ġshould Ġbe Ġrestored . ĠAn Ġexample Ġis Ġshown Ġin ĠFig . Ġ: Ġthe Ġmodel Ġwith Ġdistant Ġrepresentation Ġclass ifies Ġthe Ġpunct uation Ġafter ĠâĢ Ŀ her âĢ Ŀ Ġas Ġa ĠâĢ Ŀ COM MA âĢ Ŀ Ġbecause ĠâĢ Ŀ Before âĢ Ŀ Ġis Ġthe Ġword Ġthat Ġindicates Ġthe Ġdependent Ġclause . ĠMeanwhile , Ġthe Ġmodel Ġwithout Ġdistant Ġrepresentation Ġpredicts Ġthe Ġpunct uation Ġas Ġa ĠâĢ Ŀ PER I OD âĢ Ŀ Ġbecause Ġthere Ġis Ġno Ġself - att ention Ġmodule ; Ġtherefore , Ġit Ġdoes Ġnot Ġfocus Ġon Ġthe Ġword ĠâĢ Ŀ Before âĢ Ŀ . ĠOverall , Ġthe Ġmodel Ġthat Ġincludes Ġboth Ġlocal Ġand Ġdistant Ġrepresentation Ġcan Ġgenerally Ġbe Ġused Ġfor Ġboth Ġsentence Ġsegment ation Ġand Ġpunct uation Ġrestoration , Ġand Ġit Ġoutper forms Ġboth Ġbaseline Ġmodels . </s> result / cher ry pick d istant </s> Effect Ġof ĠCross - View ĠTraining Ġ( CV T ) </s> To Ġidentify Ġthe Ġimprovement Ġfrom ĠCV T , Ġwe Ġcompared Ġthe Ġmodels Ġthat Ġuse Ġdifferent Ġtraining Ġprocesses : Ġstandard Ġsupervised Ġtraining Ġ( ĠIN LINE FORM 0 Ġ) Ġand ĠCV T Ġ( ĠIN LINE FORM 1 Ġ). ĠThe Ġmodel Ġtrained Ġwith ĠCV T Ġimproves Ġthe Ġaccuracy Ġin Ġterms Ġof Ġthe ĠF 1 Ġscore Ġon Ġboth ĠThai Ġand ĠEnglish Ġdatasets , Ġas Ġshown Ġin Ġtab : th aires ult , tab : eng result Ġ( row Ġ( g ) Ġvs Ġrow Ġ( h )). </s> This Ġexperiment Ġwas Ġconducted Ġonly Ġon Ġthe ĠU G WC Ġdataset Ġbecause Ġno Ġunl abel ed Ġdata Ġare Ġavailable Ġin Ġthe ĠOr chid Ġdataset , Ġas Ġmentioned Ġin ĠSection ĠUID 33 Ġ. ĠThe Ġmodel Ġimproves Ġthe ĠF 1 Ġscore Ġslightly , Ġfrom Ġ88 . 8 % Ġ( row Ġ( g )) Ġto Ġ88 . 9 % Ġ( row Ġ( h )) Ġon Ġthe ĠU G WC Ġdataset . ĠThis Ġresult Ġoccurs Ġbecause Ġboth Ġthe Ġlabeled Ġand Ġunl abel ed Ġdata Ġin Ġthe ĠU G WC Ġdataset Ġare Ġdrawn Ġfrom Ġthe Ġsame Ġfinance Ġdomain . ĠThe Ġaverage Ġnumber Ġof Ġnew Ġwords Ġfound Ġin Ġa Ġnew Ġunl abel ed Ġdata Ġpassage Ġis Ġonly Ġ0 . 650 , Ġas Ġshown Ġin ĠTable ĠT AB REF 32 Ġ. ĠTherefore , Ġthere Ġis Ġlittle Ġadditional Ġinformation Ġto Ġbe Ġlearned Ġfrom Ġunl abel ed Ġdata . </s> CV T Ġalso Ġimproved Ġthe Ġmodel Ġon Ġthe ĠI W SL T Ġdataset , Ġfrom Ġan Ġoverall ĠF 1 Ġscore Ġof Ġ64 . 5 % Ġ( row Ġ( g )) Ġto Ġ65 . 3 % Ġ( row Ġ( h )) Ġand Ġfrom Ġa Ġ2 - class ĠF 1 Ġscore Ġof Ġ81 . 7 % Ġto Ġ82 . 7 %. ĠBecause Ġboth Ġthe Ġlabeled Ġand Ġunl abel ed Ġdata Ġwere Ġcollected Ġfrom ĠTED Ġtalks , Ġthe Ġnumber Ġof Ġvocabulary Ġwords Ġgrows Ġsubstantially Ġmore Ġthan Ġin Ġthe ĠU G WC Ġdataset Ġbecause Ġthe Ġtalks Ġcover Ġvarious Ġtopics . ĠIn Ġthis Ġdataset , Ġaverage Ġ1 . 225 Ġnew Ġwords Ġfound Ġin Ġeach Ġnew Ġunl abel ed Ġdata Ġpassage , Ġas Ġshown Ġin ĠTable ĠT AB REF 32 Ġ; Ġconsequently Ġthe Ġmodel Ġrepresentation Ġlearns Ġnew Ġinformation Ġfrom Ġthese Ġnew Ġwords Ġeffectively . </s> Compar ison Ġwith Ġbaseline Ġmodels </s> For Ġthe ĠThai Ġsentence Ġsegment ation Ġtask , Ġour Ġmodel Ġis Ġsuperior Ġto Ġall Ġthe Ġbas elines Ġon Ġboth ĠThai Ġsentence Ġsegment ation Ġdatasets , Ġas Ġshown Ġin ĠTable ĠT AB REF 45 Ġ. ĠOn Ġthe ĠOr chid Ġdataset , Ġthe Ġsupervised Ġmodel Ġthat Ġincludes Ġboth Ġlocal Ġand Ġdistant Ġrepresentation Ġwas Ġadopted Ġfor Ġcomparison Ġto Ġthe Ġbaseline Ġmodel . ĠOur Ġmodel Ġimproves Ġthe ĠF 1 Ġscore Ġachieved Ġby ĠCR F - ng ram , Ġwhich Ġis Ġthe Ġstate - of - the - art Ġmodel Ġfor ĠThai Ġsentence Ġsegment ation Ġin ĠOr chid , Ġfrom Ġ91 . 9 % Ġ( row Ġ( d )) Ġto Ġ92 . 5 % Ġ( row Ġ( g )). ĠMeanwhile , Ġin Ġthe ĠU G WC Ġdataset , Ġour ĠCV T Ġmodel Ġ( row Ġ( h )) Ġachieves Ġan ĠF 1 Ġscore Ġof Ġ88 . 9 %, Ġwhich Ġis Ġhigher Ġthan Ġthe ĠF 1 Ġscore Ġof Ġboth Ġthe Ġbas elines Ġ( CR F - ng ram Ġand ĠBi - L ST M - CR F Ġ( rows Ġd Ġand Ġe , Ġrespectively )). ĠThus , Ġour Ġmodel Ġis Ġnow Ġthe Ġstate - of - the - art Ġmodel Ġfor ĠThai Ġsentence Ġsegment ation Ġon Ġboth Ġthe ĠOr chid Ġand ĠU G WC Ġdatasets . </s> Our Ġmodel Ġoutper forms Ġall Ġthe Ġsequence Ġtagging Ġmodels . ĠT - BR NN - pre Ġ( row Ġ( c )) Ġis Ġthe Ġcurrent Ġstate - of - the - art Ġmodel , Ġas Ġshown Ġin ĠTable ĠT AB REF 47 Ġ. ĠThe ĠCV T Ġmodel Ġimproves Ġthe Ġoverall ĠF 1 Ġscore Ġfrom Ġthe Ġ64 . 4 % Ġof ĠT - BR NN - pre Ġto Ġ65 . 3 % Ġ( row Ġ( h )), Ġdespite Ġthe Ġfact Ġthat ĠT - BR NN - pre Ġintegrates Ġa Ġpret rained Ġword Ġvector . ĠMoreover , Ġour Ġmodel Ġalso Ġachieves Ġa Ġ2 - class ĠF 1 Ġscore Ġ1 . 3 % Ġhigher Ġthan Ġthat Ġof ĠBi - L ST M - CR F Ġ( row Ġ( e )). </s> Con clusions </s> In Ġthis Ġpaper , Ġwe Ġpropose Ġa Ġnovel Ġdeep Ġlearning Ġmodel Ġfor ĠThai Ġsentence Ġsegment ation . ĠThis Ġstudy Ġmakes Ġthree Ġmain Ġcontributions . ĠThe Ġfirst Ġcontribution Ġis Ġto Ġintegrate Ġa Ġlocal Ġrepresentation Ġbased Ġon Ġn - gram Ġembed ding Ġinto Ġour Ġdeep Ġmodel . ĠThis Ġapproach Ġhelps Ġto Ġcapture Ġword Ġgroups Ġnear Ġsentence Ġboundaries , Ġallowing Ġthe Ġmodel Ġto Ġidentify Ġboundaries Ġmore Ġaccurately . ĠSecond , Ġwe Ġintegrate Ġa Ġdistant Ġrepresentation Ġobtained Ġfrom Ġself - att ention Ġmodules Ġto Ġcapture Ġsentence Ġcontextual Ġinformation . ĠThis Ġapproach Ġallows Ġthe Ġmodel Ġto Ġfocus Ġon Ġthe Ġinitial Ġwords Ġof Ġdependent Ġclauses Ġ( i . e ., ĠâĢ Ŀ Before âĢ Ŀ , ĠâĢ Ŀ If âĢ Ŀ , Ġand ĠâĢ Ŀ Although âĢ Ŀ ). ĠThe Ġlast Ġcontribution Ġis Ġan Ġadaptation Ġof ĠCV T , Ġwhich Ġallows Ġthe Ġmodel Ġto Ġutilize Ġunl abel ed Ġdata Ġto Ġproduce Ġeffective Ġlocal Ġand Ġdistant Ġrepresentations . </s> The Ġexperiment Ġwas Ġconducted Ġon Ġtwo ĠThai Ġdatasets , ĠOr chid Ġand ĠU G WC , Ġand Ġone ĠEnglish Ġpunct uation Ġrestoration Ġdataset , ĠI W SL T . ĠEnglish Ġpunct uation Ġrestoration Ġis Ġsimilar Ġto Ġour ĠThai Ġsentence Ġsegment ation . ĠOn Ġthe ĠThai Ġsentence Ġsegment ation Ġtask , Ġour Ġmodel Ġachieves ĠF 1 Ġscores Ġof Ġ92 . 5 % Ġand Ġ88 . 9 % Ġon Ġthe ĠOr chid Ġand ĠU G WC Ġdatasets , Ġconstit uting Ġa Ġrelative Ġerror Ġreduction Ġof Ġ7 . 4 % Ġand Ġ10 . 5 %, Ġrespectively . ĠOn Ġthe ĠEnglish Ġpunct uation Ġtask , Ġthe Ġ2 - class ĠF 1 Ġscore Ġreached Ġ82 . 7 % Ġwhen Ġconsidering Ġonly Ġtwo Ġpunct uation Ġclasses Ġ( making Ġthe Ġtask Ġsimilar Ġto Ġsentence Ġsegment ation Ġin ĠThai ). ĠMoreover , Ġour Ġmodel Ġoutper forms Ġthe Ġmodel Ġintegrated Ġwith Ġpret rained Ġword Ġvectors Ġin Ġterms Ġof Ġthe Ġoverall ĠF 1 Ġscore Ġon Ġthe ĠI W SL T Ġdataset . ĠBased Ġon Ġour Ġcontributions , Ġthe Ġlocal Ġrepresentation Ġscheme Ġhas Ġthe Ġhighest Ġimpact Ġon Ġthe ĠThai Ġcorpus , Ġwhile Ġthe Ġdistant Ġrepresentation Ġand ĠCV T Ġresult Ġin Ġstrong Ġimprovements Ġon Ġthe ĠEnglish Ġdataset . </s> Moreover , Ġour Ġmodel Ġcan Ġalso Ġbe Ġapplied Ġto Ġelementary Ġdiscourse Ġunit Ġ( ED U ) Ġsegment ation , Ġwhich Ġis Ġused Ġas Ġthe Ġminimal Ġsynt actic Ġunit Ġfor Ġdownstream Ġtasks Ġsuch Ġas Ġtext Ġsummar ization Ġand Ġmachine Ġtranslation . ĠHowever , Ġno Ġexperiments Ġhave Ġbeen Ġconducted Ġto Ġdetermine Ġhow Ġdifferent Ġsentences Ġand ĠED Us Ġaffect Ġdownstream Ġtasks . ĠTherefore , Ġthe Ġevaluation Ġof Ġdownstream Ġtasks Ġfrom Ġdifferent Ġsources Ġneeds Ġto Ġbe Ġstudied . </s> Acknowled gment </s> This Ġpaper Ġwas Ġsupported Ġby ĠKL abs Ġat ĠKas ik orn ĠBusiness ĠTechnology Ġ( K BT G ), Ġwho Ġprovided Ġfacilities Ġand Ġdata . ĠThe Ġprocedures Ġthat Ġwere Ġconducted Ġbased Ġon Ġsocial Ġdata Ġare Ġvisible Ġto Ġthe Ġpublic , Ġand Ġethical Ġissues Ġthat Ġcan Ġarise Ġfrom Ġthe Ġuse Ġof Ġsuch Ġdata Ġwere Ġaddressed . ĠWe Ġwould Ġlike Ġto Ġthank Ġthe Ġlingu ists ĠSas iw imon ĠKal uns ima , ĠN utch a ĠTir as aro j , ĠTant ong ĠCh ampa ib oon Ġand ĠSup aw at ĠTa er ung ru ang Ġfor Ġannot ating Ġthe ĠU G WC Ġdataset Ġused Ġin Ġthis Ġstudy . </s> Hyper param eters </s> The Ġhyper param eter Ġvalues Ġwere Ġdetermined Ġthrough Ġa Ġgrid Ġsearch Ġto Ġfind Ġtheir Ġoptimal Ġvalues Ġon Ġthe Ġdifferent Ġdatasets . ĠAll Ġthe Ġhyper param eters Ġfor Ġeach Ġdataset Ġare Ġshown Ġin ĠTable ĠT AB REF 55 Ġ. ĠThe Ġoptimal Ġvalues Ġfrom Ġthe Ġgrid Ġsearch Ġdepend Ġon Ġthe Ġtask . ĠFor ĠThai Ġsentence Ġsegment ation , Ġthe Ġhyper param eters Ġare Ġtuned Ġto Ġobtain Ġthe Ġhighest Ġsentence Ġboundary ĠF 1 Ġscore , Ġwhile Ġthe Ġoverall ĠF 1 Ġscore Ġis Ġused Ġto Ġtune Ġthe Ġparameters Ġfor ĠEnglish Ġpunct uation Ġrestoration . </s> Compar ison Ġof ĠCNN Ġand Ġn - gram Ġmodels Ġfor Ġlocal Ġrepresentation </s> Jac ovi ĠA . Ġet Ġal . ĠB IB REF 13 Ġproposed Ġthat Ġa ĠCNN Ġcan Ġbe Ġused Ġas Ġan Ġn - gram Ġdetector Ġto Ġcapture Ġlocal Ġtext Ġfeatures . ĠTherefore , Ġwe Ġalso Ġperformed Ġan Ġexperiment Ġto Ġcompare Ġa ĠCNN Ġand Ġn - gram Ġembedded Ġas Ġlocal Ġstructures . ĠThe Ġresults Ġin ĠTable ĠT AB REF 56 Ġshow Ġthat Ġthe Ġmodel Ġusing Ġthe Ġembedded Ġn - gram Ġyields Ġgreater Ġimprovement Ġthan Ġthe Ġone Ġusing Ġan Ġembedded ĠCNN Ġon Ġthe ĠOr chid Ġand ĠU G WC Ġdatasets . </s> Stat istical ĠTests Ġfor ĠThai Ġsentence Ġsegment ation </s> To Ġprove Ġthe Ġsignificance Ġof Ġthe Ġmodel Ġimprovements , Ġwe Ġcompared Ġthe Ġcross - valid ation Ġresults Ġusing Ġpaired Ġt - tests Ġto Ġobtain Ġthe Ġp - values , Ġwhich Ġare Ġshown Ġin ĠTable ĠT AB REF 57 Ġfor Ġthe ĠOr chid Ġdataset Ġand ĠTable ĠT AB REF 58 Ġfor Ġthe ĠU G WC Ġdataset .