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Introduction </s> Twitter Ġis Ġa Ġwidely Ġused Ġmicro blog ging Ġplatform , Ġwhere Ġusers Ġpost Ġand Ġinteract Ġwith Ġmessages , ĠâĢ ľ t we ets âĢ Ŀ . ĠUnderstanding Ġthe Ġsemantic Ġrepresentation Ġof Ġtweets Ġcan Ġbenefit Ġa Ġplethora Ġof Ġapplications Ġsuch Ġas Ġsentiment Ġanalysis ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, Ġhashtag Ġprediction ĠB IB REF 2 Ġ, Ġparaph rase Ġdetection ĠB IB REF 3 Ġand Ġmicro blog Ġranking ĠB IB REF 4 Ġ, ĠB IB REF 5 Ġ. ĠHowever , Ġtweets Ġare Ġdifficult Ġto Ġmodel Ġas Ġthey Ġpose Ġseveral Ġchallenges Ġsuch Ġas Ġshort Ġlength , Ġinformal Ġwords , Ġunusual Ġgrammar Ġand Ġmiss pell ings . ĠRecently , Ġresearchers Ġare Ġfocusing Ġon Ġleveraging Ġun super vised Ġrepresentation Ġlearning Ġmethods Ġbased Ġon Ġneural Ġnetworks Ġto Ġsolve Ġthis Ġproblem . ĠOnce Ġthese Ġrepresentations Ġare Ġlearned , Ġwe Ġcan Ġuse Ġoff - the - she lf Ġpredict ors Ġtaking Ġthe Ġrepresentation Ġas Ġinput Ġto Ġsolve Ġthe Ġdownstream Ġtask ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ. ĠThese Ġmethods Ġenjoy Ġseveral Ġadvantages : Ġ( 1 ) Ġthey Ġare Ġcheaper Ġto Ġtrain , Ġas Ġthey Ġwork Ġwith Ġunl ab elled Ġdata , Ġ( 2 ) Ġthey Ġreduce Ġthe Ġdependence Ġon Ġdomain Ġlevel Ġexperts , Ġand Ġ( 3 ) Ġthey Ġare Ġhighly Ġeffective Ġacross Ġmultiple Ġapplications , Ġin Ġpractice . </s> Despite Ġthis , Ġthere Ġis Ġa Ġlack Ġof Ġprior Ġwork Ġwhich Ġsurveys Ġthe Ġtweet - specific Ġun super vised Ġrepresentation Ġlearning Ġmodels . ĠIn Ġthis Ġwork , Ġwe Ġattempt Ġto Ġfill Ġthis Ġgap Ġby Ġinvestigating Ġthe Ġmodels Ġin Ġan Ġorganized Ġfashion . ĠSpecifically , Ġwe Ġgroup Ġthe Ġmodels Ġbased Ġon Ġthe Ġobjective Ġfunction Ġit Ġoptim izes . ĠWe Ġbelieve Ġthis Ġwork Ġcan Ġaid Ġthe Ġunderstanding Ġof Ġthe Ġexisting Ġliterature . ĠWe Ġconclude Ġthe Ġpaper Ġby Ġpresenting Ġinteresting Ġfuture Ġresearch Ġdirections , Ġwhich Ġwe Ġbelieve Ġare Ġfruitful Ġin Ġadvancing Ġthis Ġfield Ġby Ġbuilding Ġhigh - quality Ġtweet Ġrepresentation Ġlearning Ġmodels . </s> Un super vised ĠTweet ĠRepresent ation ĠModels </s> There Ġare Ġvarious Ġmodels Ġspanning Ġacross Ġdifferent Ġmodel Ġarchitectures Ġand Ġobjective Ġfunctions Ġin Ġthe Ġliterature Ġto Ġcompute Ġtweet Ġrepresentation Ġin Ġan Ġun super vised Ġfashion . ĠThese Ġmodels Ġwork Ġin Ġa Ġsemi - super vised Ġway Ġ- Ġthe Ġrepresentations Ġgenerated Ġby Ġthe Ġmodel Ġis Ġfed Ġto Ġan Ġoff - the - she lf Ġpredictor Ġlike ĠSupport ĠVector ĠMachines Ġ( S VM ) Ġto Ġsolve Ġa Ġparticular Ġdownstream Ġtask . ĠThese Ġmodels Ġspan Ġacross Ġa Ġwide Ġvariety Ġof Ġneural Ġnetwork Ġbased Ġarchitectures Ġincluding Ġaverage Ġof Ġword Ġvectors , Ġconv olution al - based , Ġrecurrent - based Ġand Ġso Ġon . ĠWe Ġbelieve Ġthat Ġthe Ġperformance Ġof Ġthese Ġmodels Ġis Ġhighly Ġdependent Ġon Ġthe Ġobjective Ġfunction Ġit Ġoptim izes ĠâĢĵ Ġpredicting Ġadjacent Ġword Ġ( within - t weet Ġrelationships ), Ġadjacent Ġtweet Ġ( inter - t weet Ġrelationships ), Ġthe Ġtweet Ġitself Ġ( aut oen c oder ), Ġmodeling Ġfrom Ġstructured Ġresources Ġlike Ġparaph rase Ġdatabases Ġand Ġweak Ġsupervision . ĠIn Ġthis Ġsection , Ġwe Ġprovide Ġthe Ġfirst Ġof Ġits Ġkind Ġsurvey Ġof Ġthe Ġrecent Ġtweet - specific Ġun super vised Ġmodels Ġin Ġan Ġorganized Ġfashion Ġto Ġunderstand Ġthe Ġliterature . ĠSpecifically , Ġwe Ġcategor ize Ġeach Ġmodel Ġbased Ġon Ġthe Ġoptimized Ġobjective Ġfunction Ġas Ġshown Ġin ĠFigure ĠFIG REF 1 Ġ. ĠNext , Ġwe Ġstudy Ġeach Ġcategory Ġone Ġby Ġone . </s> Mod eling Ġwithin - t weet Ġrelationships </s> Mot ivation : ĠEvery Ġtweet Ġis Ġassumed Ġto Ġhave Ġa Ġlatent Ġtopic Ġvector , Ġwhich Ġinfluences Ġthe Ġdistribution Ġof Ġthe Ġwords Ġin Ġthe Ġtweet . ĠFor Ġexample , Ġthough Ġthe Ġappearance Ġof Ġthe Ġphrase Ġcatch Ġthe Ġball Ġis Ġfrequent Ġin Ġthe Ġcorpus , Ġif Ġwe Ġknow Ġthat Ġthe Ġtopic Ġof Ġa Ġtweet Ġis Ġabout ĠâĢ ľ technology âĢ Ŀ , Ġwe Ġcan Ġexpect Ġwords Ġsuch Ġas Ġbug Ġor Ġexception Ġafter Ġthe Ġword Ġcatch Ġ( ign oring Ġthe ) Ġinstead Ġof Ġthe Ġword Ġball Ġsince Ġcatch Ġthe Ġbug / ex ception Ġis Ġmore Ġplausible Ġunder Ġthe Ġtopic ĠâĢ ľ technology âĢ Ŀ . ĠOn Ġthe Ġother Ġhand , Ġif Ġthe Ġtopic Ġof Ġthe Ġtweet Ġis Ġabout ĠâĢ ľ sports âĢ Ŀ , Ġthen Ġwe Ġcan Ġexpect Ġball Ġafter Ġcatch . ĠThese Ġintu itions Ġindicate Ġthat Ġthe Ġprediction Ġof Ġneighboring Ġwords Ġfor Ġa Ġgiven Ġword Ġstrongly Ġrelies Ġon Ġthe Ġtweet Ġalso . </s> Mod els : ĠB IB REF 8 Ġ' s Ġwork Ġis Ġthe Ġfirst Ġto Ġexploit Ġthis Ġidea Ġto Ġcompute Ġdistributed Ġdocument Ġrepresentations Ġthat Ġare Ġgood Ġat Ġpredicting Ġwords Ġin Ġthe Ġdocument . ĠThey Ġpropose Ġtwo Ġmodels : ĠPV - DM Ġand ĠPV - DB OW , Ġthat Ġare Ġextensions Ġof ĠContinuous ĠBag ĠOf ĠWords Ġ( CB OW ) Ġand ĠSkip - gram Ġmodel Ġvariants Ġof Ġthe Ġpopular ĠWord 2 V ec Ġmodel ĠB IB REF 9 Ġrespectively ĠâĢĵ ĠPV - DM Ġinserts Ġan Ġadditional Ġdocument Ġtoken Ġ( which Ġcan Ġbe Ġthought Ġof Ġas Ġanother Ġword ) Ġwhich Ġis Ġshared Ġacross Ġall Ġcontexts Ġgenerated Ġfrom Ġthe Ġsame Ġdocument ; ĠPV - DB OW Ġattempts Ġto Ġpredict Ġthe Ġsampled Ġwords Ġfrom Ġthe Ġdocument Ġgiven Ġthe Ġdocument Ġrepresentation . ĠAlthough Ġoriginally Ġemployed Ġfor Ġparagraphs Ġand Ġdocuments , Ġthese Ġmodels Ġwork Ġbetter Ġthan Ġthe Ġtraditional Ġmodels : ĠB OW ĠB IB REF 10 Ġand ĠL DA ĠB IB REF 11 Ġfor Ġtweet Ġclassification Ġand Ġmicro blog Ġretrieval Ġtasks ĠB IB REF 12 Ġ. ĠThe Ġauthors Ġin ĠB IB REF 12 Ġmake Ġthe ĠPV - DM Ġand ĠPV - DB OW Ġmodels Ġconcept - aware Ġ( a Ġrich Ġsemantic Ġsignal Ġfrom Ġa Ġtweet ) Ġby Ġaugment ing Ġtwo Ġfeatures : Ġattention Ġover Ġcontextual Ġwords Ġand Ġconceptual Ġtweet Ġembed ding , Ġwhich Ġjointly Ġexploit Ġconcept - level Ġsenses Ġof Ġtweets Ġto Ġcompute Ġbetter Ġrepresentations . ĠBoth Ġthe Ġdiscussed Ġworks Ġhave Ġthe Ġfollowing Ġcharacteristics : Ġ( 1 ) Ġthey Ġuse Ġa Ġshallow Ġarchitecture , Ġwhich Ġenables Ġfast Ġtraining , Ġ( 2 ) Ġcomputing Ġrepresentations Ġfor Ġtest Ġtweets Ġrequires Ġcomputing Ġgrad ients , Ġwhich Ġis Ġtime - consuming Ġfor Ġreal - time ĠTwitter Ġapplications , Ġand Ġ( 3 ) Ġmost Ġimportantly , Ġthey Ġfail Ġto Ġexploit Ġtextual Ġinformation Ġfrom Ġrelated Ġtweets Ġthat Ġcan Ġbear Ġsalient Ġsemantic Ġsignals . </s> Mod eling Ġinter - t weet Ġrelationships </s> Mot ivation : ĠTo Ġcapture Ġrich Ġtweet Ġsemantics , Ġresearchers Ġare Ġattempting Ġto Ġexploit Ġa Ġtype Ġof Ġsentence - level ĠDistribution al ĠHyp ot hesis ĠB IB REF 10 Ġ, ĠB IB REF 13 Ġ. ĠThe Ġidea Ġis Ġto Ġinfer Ġthe Ġtweet Ġrepresentation Ġfrom Ġthe Ġcontent Ġof Ġadjacent Ġtweets Ġin Ġa Ġrelated Ġstream Ġlike Ġusers ' ĠTwitter Ġtimeline , Ġtopical , Ġretweet Ġand Ġconvers ational Ġstream . ĠThis Ġapproach Ġsignificantly Ġallev iates Ġthe Ġcontext Ġins uff iciency Ġproblem Ġcaused Ġdue Ġto Ġthe Ġambiguous Ġand Ġshort Ġnature Ġof Ġtweets ĠB IB REF 0 Ġ, ĠB IB REF 14 Ġ. </s> Mod els : ĠSkip - thought Ġvectors ĠB IB REF 15 Ġ( ST V ) Ġis Ġa Ġwidely Ġpopular Ġsentence Ġenc oder , Ġwhich Ġis Ġtrained Ġto Ġpredict Ġadjacent Ġsentences Ġin Ġthe Ġbook Ġcorpus ĠB IB REF 16 Ġ. ĠAlthough Ġthe Ġtesting Ġis Ġcheap Ġas Ġit Ġinvolves Ġa Ġcheap Ġforward Ġpropagation Ġof Ġthe Ġtest Ġsentence , ĠST V Ġis Ġvery Ġslow Ġto Ġtrain Ġthanks Ġto Ġits Ġcomplicated Ġmodel Ġarchitecture . ĠTo Ġcombat Ġthis Ġcomputational Ġin efficiency , ĠFast Sent ĠB IB REF 17 Ġpropose Ġa Ġsimple Ġadditive Ġ( log - linear ) Ġsentence Ġmodel , Ġwhich Ġpredicts Ġadjacent Ġsentences Ġ( represented Ġas ĠB OW ) Ġtaking Ġthe ĠB OW Ġrepresentation Ġof Ġsome Ġsentence Ġin Ġcontext . ĠThis Ġmodel Ġcan Ġexploit Ġthe Ġsame Ġsignal , Ġbut Ġat Ġa Ġmuch Ġlower Ġcomputational Ġexpense . ĠParallel Ġto Ġthis Ġwork , ĠS iam ase ĠCB OW ĠB IB REF 18 Ġdevelop Ġa Ġmodel Ġwhich Ġdirectly Ġcompares Ġthe ĠB OW Ġrepresentation Ġof Ġtwo Ġsentence Ġto Ġbring Ġthe Ġembed d ings Ġof Ġa Ġsentence Ġcloser Ġto Ġits Ġadjacent Ġsentence , Ġaway Ġfrom Ġa Ġrandomly Ġoccurring Ġsentence Ġin Ġthe Ġcorpus . ĠFor ĠFast Sent Ġand ĠSi ames e ĠCB OW , Ġthe Ġtest Ġsentence Ġrepresentation Ġis Ġa Ġsimple Ġaverage Ġof Ġword Ġvectors Ġobtained Ġafter Ġtraining . ĠBoth Ġof Ġthese Ġmodels Ġare Ġgeneral Ġpurpose Ġsentence Ġrepresentation Ġmodels Ġtrained Ġon Ġbook Ġcorpus , Ġyet Ġgive Ġa Ġcompetitive Ġperformance Ġover Ġprevious Ġmodels Ġon Ġthe Ġtweet Ġsemantic Ġsimilarity Ġcomputation Ġtask . ĠB IB REF 14 Ġ' s Ġmodel Ġattempt Ġto Ġexploit Ġthese Ġsignals Ġdirectly Ġfrom ĠTwitter . ĠWith Ġthe Ġhelp Ġof Ġattention Ġtechnique Ġand Ġlearned Ġuser Ġrepresentation , Ġthis Ġlog - linear Ġmodel Ġis Ġable Ġto Ġcapture Ġsalient Ġsemantic Ġinformation Ġfrom Ġchron ologically Ġadjacent Ġtweets Ġof Ġa Ġtarget Ġtweet Ġin Ġusers ' ĠTwitter Ġtimeline . </s> Mod eling Ġfrom Ġstructured Ġresources </s> Mot ivation : ĠIn Ġrecent Ġtimes , Ġbuilding Ġrepresentation Ġmodels Ġbased Ġon Ġsupervision Ġfrom Ġrich ly Ġstructured Ġresources Ġsuch Ġas ĠPar aph rase ĠDatabase Ġ( P PD B ) ĠB IB REF 19 Ġ( containing Ġnoisy Ġphrase Ġpairs ) Ġhas Ġyielded Ġhigh Ġquality Ġsentence Ġrepresentations . ĠThese Ġmethods Ġwork Ġby Ġmaximizing Ġthe Ġsimilarity Ġof Ġthe Ġsentences Ġin Ġthe Ġlearned Ġsemantic Ġspace . </s> Mod els : ĠCHAR AG RAM ĠB IB REF 20 Ġembed s Ġtextual Ġsequences Ġby Ġlearning Ġa Ġcharacter - based Ġcompos itional Ġmodel Ġthat Ġinvolves Ġaddition Ġof Ġthe Ġvectors Ġof Ġits Ġcharacter Ġn - gram s Ġfollowed Ġby Ġan Ġelement wise Ġnon linear ity . ĠThis Ġsimpler Ġarchitecture Ġtrained Ġon ĠP PD B Ġis Ġable Ġto Ġbeat Ġmodels Ġwith Ġcomplex Ġarchitectures Ġlike ĠCNN , ĠL ST M Ġon ĠSem E val Ġ2015 ĠTwitter Ġtextual Ġsimilarity Ġtask Ġby Ġa Ġlarge Ġmargin . ĠThis Ġresult Ġemphasizes Ġthe Ġimportance Ġof Ġcharacter - level Ġmodels Ġthat Ġaddress Ġdifferences Ġdue Ġto Ġspelling Ġvariation Ġand Ġword Ġchoice . ĠThe Ġauthors Ġin Ġtheir Ġsubsequent Ġwork ĠB IB REF 21 Ġconduct Ġa Ġcomprehensive Ġanalysis Ġof Ġmodels Ġspanning Ġthe Ġrange Ġof Ġcomplexity Ġfrom Ġword Ġaveraging Ġto ĠL ST Ms Ġfor Ġits Ġability Ġto Ġdo Ġtransfer Ġand Ġsupervised Ġlearning Ġafter Ġoptimizing Ġa Ġmargin Ġbased Ġloss Ġon ĠP PD B . ĠFor Ġtransfer Ġlearning , Ġthey Ġfind Ġmodels Ġbased Ġon Ġword Ġaveraging Ġperform Ġwell Ġon Ġboth Ġthe Ġin - domain Ġand Ġout - of - domain Ġtextual Ġsimilarity Ġtasks , Ġbeating ĠL ST M Ġmodel Ġby Ġa Ġlarge Ġmargin . ĠOn Ġthe Ġother Ġhand , Ġthe Ġword Ġaveraging Ġmodels Ġperform Ġwell Ġfor Ġboth Ġsentence Ġsimilarity Ġand Ġtextual Ġentail ment Ġtasks , Ġoutper forming Ġthe ĠL ST M . ĠHowever , Ġfor Ġsentiment Ġclassification Ġtask , Ġthey Ġfind ĠL ST M Ġ( trained Ġon ĠP PD B ) Ġto Ġbeat Ġthe Ġaveraging Ġmodels Ġto Ġestablish Ġa Ġnew Ġstate Ġof Ġthe Ġart . ĠThe Ġabove Ġresults Ġsuggest Ġthat Ġstructured Ġresources Ġplay Ġa Ġvital Ġrole Ġin Ġcomputing Ġgeneral - purpose Ġembed d ings Ġuseful Ġin Ġdownstream Ġapplications . </s> Mod eling Ġas Ġan Ġaut oen c oder </s> Mot ivation : ĠThe Ġaut oen c oder Ġbased Ġapproach Ġlearns Ġlatent Ġ( or Ġcompressed ) Ġrepresentation Ġby Ġreconstruct ing Ġits Ġown Ġinput . ĠSince Ġtextual Ġdata Ġlike Ġtweets Ġcontain Ġdiscrete Ġinput Ġsignals , Ġsequence - to - sequence Ġmodels ĠB IB REF 22 Ġlike ĠST V Ġcan Ġbe Ġused Ġto Ġbuild Ġthe Ġsolution . ĠThe Ġenc oder Ġmodel Ġwhich Ġenc odes Ġthe Ġinput Ġtweet Ġcan Ġtypically Ġbe Ġa ĠCNN ĠB IB REF 23 Ġ, Ġrecurrent Ġmodels Ġlike ĠR NN , ĠGR U , ĠL ST M ĠB IB REF 24 Ġor Ġmemory Ġnetworks ĠB IB REF 25 Ġ. ĠThe Ġdec oder Ġmodel Ġwhich Ġgenerates Ġthe Ġoutput Ġtweet Ġcan Ġtypically Ġbe Ġa Ġrecurrent Ġmodel Ġthat Ġpredicts Ġa Ġoutput Ġtoken Ġat Ġevery Ġtime Ġstep . </s> Mod els : ĠSequ ential ĠDen o ising ĠAut oen cod ers Ġ( S DA E ) ĠB IB REF 17 Ġis Ġa ĠL ST M - based Ġsequence - to - sequence Ġmodel , Ġwhich Ġis Ġtrained Ġto Ġrecover Ġthe Ġoriginal Ġdata Ġfrom Ġthe Ġcorrupted Ġversion . ĠS DA E Ġproduces Ġrobust Ġrepresentations Ġby Ġlearning Ġto Ġrepresent Ġthe Ġdata Ġin Ġterms Ġof Ġfeatures Ġthat Ġexplain Ġits Ġimportant Ġfactors Ġof Ġvariation . ĠTweet 2 V ec ĠB IB REF 3 Ġis Ġa Ġrecent Ġmodel Ġwhich Ġuses Ġa Ġcharacter - level ĠCNN - L ST M Ġenc oder - dec oder Ġarchitecture Ġtrained Ġto Ġconstruct Ġthe Ġinput Ġtweet Ġdirectly . ĠThis Ġmodel Ġoutper forms Ġcompetitive Ġmodels Ġthat Ġwork Ġon Ġword - level Ġlike ĠPV - DM , ĠPV - DB OW Ġon Ġsemantic Ġsimilarity Ġcomputation Ġand Ġsentiment Ġclassification Ġtasks , Ġthereby Ġshowing Ġthat Ġthe Ġcharacter - level Ġnature Ġof ĠTweet 2 V ec Ġis Ġbest - su ited Ġto Ġdeal Ġwith Ġthe Ġnoise Ġand Ġidiosyncr as ies Ġof Ġtweets . ĠTweet 2 V ec Ġcontrols Ġthe Ġgeneral ization Ġerror Ġby Ġusing Ġa Ġdata Ġaug mentation Ġtechnique , Ġwherein Ġtweets Ġare Ġreplicated Ġand Ġsome Ġof Ġthe Ġwords Ġin Ġthe Ġreplicated Ġtweets Ġare Ġreplaced Ġwith Ġtheir Ġsyn onyms . ĠBoth ĠS DA E Ġand ĠTweet 2 V ec Ġhas Ġthe Ġadvantage Ġthat Ġthey Ġdon 't Ġneed Ġa Ġcoherent Ġinter - sent ence Ġnarrative Ġ( like ĠST V ), Ġwhich Ġis Ġhard Ġto Ġobtain Ġin ĠTwitter . </s> Mod eling Ġusing Ġweak Ġsupervision </s> Mot ivation : ĠIn Ġa Ġweak ly Ġsupervised Ġsetup , Ġwe Ġcreate Ġlabels Ġfor Ġa Ġtweet Ġautomatically Ġand Ġpredict Ġthem Ġto Ġlearn Ġpotentially Ġsophisticated Ġmodels Ġthan Ġthose Ġobtained Ġby Ġun super vised Ġlearning Ġalone . ĠExamples Ġof Ġlabels Ġinclude Ġsentiment Ġof Ġthe Ġoverall Ġtweet , Ġwords Ġlike Ġhashtag Ġpresent Ġin Ġthe Ġtweet Ġand Ġso Ġon . ĠThis Ġtechnique Ġcan Ġcreate Ġa Ġhuge Ġlabeled Ġdataset Ġespecially Ġfor Ġbuilding Ġdata - hung ry , Ġsophisticated Ġdeep Ġlearning Ġmodels . </s> Mod els : ĠB IB REF 26 Ġlearns Ġsentiment - specific Ġword Ġembed ding Ġ( SS WE ), Ġwhich Ġenc odes Ġthe Ġpol arity Ġinformation Ġin Ġthe Ġword Ġrepresentations Ġso Ġthat Ġwords Ġwith Ġcontrasting Ġpolar ities Ġand Ġsimilar Ġsynt actic Ġcontext Ġ( like Ġgood Ġand Ġbad ) Ġare Ġpushed Ġaway Ġfrom Ġeach Ġother Ġin Ġthe Ġsemantic Ġspace Ġthat Ġit Ġlearns . ĠSS WE Ġutilizes Ġthe Ġmassive Ġdistant - super vised Ġtweets Ġcollected Ġby Ġpositive Ġand Ġnegative Ġemot icons Ġto Ġbuild Ġa Ġpowerful Ġtweet Ġrepresentation , Ġwhich Ġare Ġshown Ġto Ġbe Ġuseful Ġin Ġtasks Ġsuch Ġas Ġsentiment Ġclassification Ġand Ġword Ġsimilarity Ġcomputation Ġin Ġsentiment Ġlex icon . ĠB IB REF 2 Ġobserves Ġthat Ġhasht ags Ġin Ġtweets Ġcan Ġbe Ġconsidered Ġas Ġtopics Ġand Ġhence Ġtweets Ġwith Ġsimilar Ġhasht ags Ġmust Ġcome Ġcloser Ġto Ġeach Ġother . ĠTheir Ġmodel Ġpredicts Ġthe Ġhasht ags Ġby Ġusing Ġa ĠBi - GR U Ġlayer Ġto Ġembed Ġthe Ġtweets Ġfrom Ġits Ġcharacters . ĠDue Ġto Ġsub word Ġmodeling , Ġsuch Ġcharacter - level Ġmodels Ġcan Ġapproximate Ġthe Ġrepresentations Ġfor Ġrare Ġwords Ġand Ġnew Ġwords Ġ( words Ġnot Ġseen Ġduring Ġtraining ) Ġin Ġthe Ġtest Ġtweets Ġreally Ġwell . ĠThis Ġmodel Ġoutper forms Ġthe Ġword - level Ġbas elines Ġfor Ġhashtag Ġprediction Ġtask , Ġthereby Ġconcluding Ġthat Ġexploring Ġcharacter - level Ġmodels Ġfor Ġtweets Ġis Ġa Ġworthy Ġresearch Ġdirection Ġto Ġpursue . ĠBoth Ġthese Ġworks Ġfail Ġto Ġstudy Ġthe Ġmodel 's Ġgener ality ĠB IB REF 27 Ġ, Ġi . e ., Ġthe Ġability Ġof Ġthe Ġmodel Ġto Ġtransfer Ġthe Ġlearned Ġrepresentations Ġto Ġdiverse Ġtasks . </s> Future ĠDirections </s> In Ġthis Ġsection Ġwe Ġpresent Ġthe Ġfuture Ġresearch Ġdirections Ġwhich Ġwe Ġbelieve Ġcan Ġbe Ġworth Ġpursuing Ġto Ġgenerate Ġhigh Ġquality Ġtweet Ġembed d ings . </s> Conclusion </s> In Ġthis Ġwork Ġwe Ġstudy Ġthe Ġproblem Ġof Ġlearning Ġun super vised Ġtweet Ġrepresentations . ĠWe Ġbelieve Ġour Ġsurvey Ġof Ġthe Ġexisting Ġworks Ġbased Ġon Ġthe Ġobjective Ġfunction Ġcan Ġgive Ġvital Ġperspectives Ġto Ġresearchers Ġand Ġaid Ġtheir Ġunderstanding Ġof Ġthe Ġfield . ĠWe Ġalso Ġbelieve Ġthe Ġfuture Ġresearch Ġdirections Ġstudied Ġin Ġthis Ġwork Ġcan Ġhelp Ġin Ġbreaking Ġthe Ġbarriers Ġin Ġbuilding Ġhigh Ġquality , Ġgeneral Ġpurpose Ġtweet Ġrepresentation Ġmodels .
Introduction Twitter is a widely used microblogging platform, where users post and interact with messages, “tweets”. Understanding the semantic representation of tweets can benefit a plethora of applications such as sentiment analysis BIBREF0 , BIBREF1 , hashtag prediction BIBREF2 , paraphrase detection BIBREF3 and microblog ranking BIBREF4 , BIBREF5 . However, tweets are difficult to model as they pose several challenges such as short length, informal words, unusual grammar and misspellings. Recently, researchers are focusing on leveraging unsupervised representation learning methods based on neural networks to solve this problem. Once these representations are learned, we can use off-the-shelf predictors taking the representation as input to solve the downstream task BIBREF6 , BIBREF7 . These methods enjoy several advantages: (1) they are cheaper to train, as they work with unlabelled data, (2) they reduce the dependence on domain level experts, and (3) they are highly effective across multiple applications, in practice. Despite this, there is a lack of prior work which surveys the tweet-specific unsupervised representation learning models. In this work, we attempt to fill this gap by investigating the models in an organized fashion. Specifically, we group the models based on the objective function it optimizes. We believe this work can aid the understanding of the existing literature. We conclude the paper by presenting interesting future research directions, which we believe are fruitful in advancing this field by building high-quality tweet representation learning models. Unsupervised Tweet Representation Models There are various models spanning across different model architectures and objective functions in the literature to compute tweet representation in an unsupervised fashion. These models work in a semi-supervised way - the representations generated by the model is fed to an off-the-shelf predictor like Support Vector Machines (SVM) to solve a particular downstream task. These models span across a wide variety of neural network based architectures including average of word vectors, convolutional-based, recurrent-based and so on. We believe that the performance of these models is highly dependent on the objective function it optimizes – predicting adjacent word (within-tweet relationships), adjacent tweet (inter-tweet relationships), the tweet itself (autoencoder), modeling from structured resources like paraphrase databases and weak supervision. In this section, we provide the first of its kind survey of the recent tweet-specific unsupervised models in an organized fashion to understand the literature. Specifically, we categorize each model based on the optimized objective function as shown in Figure FIGREF1 . Next, we study each category one by one. Modeling within-tweet relationships Motivation: Every tweet is assumed to have a latent topic vector, which influences the distribution of the words in the tweet. For example, though the appearance of the phrase catch the ball is frequent in the corpus, if we know that the topic of a tweet is about “technology”, we can expect words such as bug or exception after the word catch (ignoring the) instead of the word ball since catch the bug/exception is more plausible under the topic “technology”. On the other hand, if the topic of the tweet is about “sports”, then we can expect ball after catch. These intuitions indicate that the prediction of neighboring words for a given word strongly relies on the tweet also. Models: BIBREF8 's work is the first to exploit this idea to compute distributed document representations that are good at predicting words in the document. They propose two models: PV-DM and PV-DBOW, that are extensions of Continuous Bag Of Words (CBOW) and Skip-gram model variants of the popular Word2Vec model BIBREF9 respectively – PV-DM inserts an additional document token (which can be thought of as another word) which is shared across all contexts generated from the same document; PV-DBOW attempts to predict the sampled words from the document given the document representation. Although originally employed for paragraphs and documents, these models work better than the traditional models: BOW BIBREF10 and LDA BIBREF11 for tweet classification and microblog retrieval tasks BIBREF12 . The authors in BIBREF12 make the PV-DM and PV-DBOW models concept-aware (a rich semantic signal from a tweet) by augmenting two features: attention over contextual words and conceptual tweet embedding, which jointly exploit concept-level senses of tweets to compute better representations. Both the discussed works have the following characteristics: (1) they use a shallow architecture, which enables fast training, (2) computing representations for test tweets requires computing gradients, which is time-consuming for real-time Twitter applications, and (3) most importantly, they fail to exploit textual information from related tweets that can bear salient semantic signals. Modeling inter-tweet relationships Motivation: To capture rich tweet semantics, researchers are attempting to exploit a type of sentence-level Distributional Hypothesis BIBREF10 , BIBREF13 . The idea is to infer the tweet representation from the content of adjacent tweets in a related stream like users' Twitter timeline, topical, retweet and conversational stream. This approach significantly alleviates the context insufficiency problem caused due to the ambiguous and short nature of tweets BIBREF0 , BIBREF14 . Models: Skip-thought vectors BIBREF15 (STV) is a widely popular sentence encoder, which is trained to predict adjacent sentences in the book corpus BIBREF16 . Although the testing is cheap as it involves a cheap forward propagation of the test sentence, STV is very slow to train thanks to its complicated model architecture. To combat this computational inefficiency, FastSent BIBREF17 propose a simple additive (log-linear) sentence model, which predicts adjacent sentences (represented as BOW) taking the BOW representation of some sentence in context. This model can exploit the same signal, but at a much lower computational expense. Parallel to this work, Siamase CBOW BIBREF18 develop a model which directly compares the BOW representation of two sentence to bring the embeddings of a sentence closer to its adjacent sentence, away from a randomly occurring sentence in the corpus. For FastSent and Siamese CBOW, the test sentence representation is a simple average of word vectors obtained after training. Both of these models are general purpose sentence representation models trained on book corpus, yet give a competitive performance over previous models on the tweet semantic similarity computation task. BIBREF14 's model attempt to exploit these signals directly from Twitter. With the help of attention technique and learned user representation, this log-linear model is able to capture salient semantic information from chronologically adjacent tweets of a target tweet in users' Twitter timeline. Modeling from structured resources Motivation: In recent times, building representation models based on supervision from richly structured resources such as Paraphrase Database (PPDB) BIBREF19 (containing noisy phrase pairs) has yielded high quality sentence representations. These methods work by maximizing the similarity of the sentences in the learned semantic space. Models: CHARAGRAM BIBREF20 embeds textual sequences by learning a character-based compositional model that involves addition of the vectors of its character n-grams followed by an elementwise nonlinearity. This simpler architecture trained on PPDB is able to beat models with complex architectures like CNN, LSTM on SemEval 2015 Twitter textual similarity task by a large margin. This result emphasizes the importance of character-level models that address differences due to spelling variation and word choice. The authors in their subsequent work BIBREF21 conduct a comprehensive analysis of models spanning the range of complexity from word averaging to LSTMs for its ability to do transfer and supervised learning after optimizing a margin based loss on PPDB. For transfer learning, they find models based on word averaging perform well on both the in-domain and out-of-domain textual similarity tasks, beating LSTM model by a large margin. On the other hand, the word averaging models perform well for both sentence similarity and textual entailment tasks, outperforming the LSTM. However, for sentiment classification task, they find LSTM (trained on PPDB) to beat the averaging models to establish a new state of the art. The above results suggest that structured resources play a vital role in computing general-purpose embeddings useful in downstream applications. Modeling as an autoencoder Motivation: The autoencoder based approach learns latent (or compressed) representation by reconstructing its own input. Since textual data like tweets contain discrete input signals, sequence-to-sequence models BIBREF22 like STV can be used to build the solution. The encoder model which encodes the input tweet can typically be a CNN BIBREF23 , recurrent models like RNN, GRU, LSTM BIBREF24 or memory networks BIBREF25 . The decoder model which generates the output tweet can typically be a recurrent model that predicts a output token at every time step. Models: Sequential Denoising Autoencoders (SDAE) BIBREF17 is a LSTM-based sequence-to-sequence model, which is trained to recover the original data from the corrupted version. SDAE produces robust representations by learning to represent the data in terms of features that explain its important factors of variation. Tweet2Vec BIBREF3 is a recent model which uses a character-level CNN-LSTM encoder-decoder architecture trained to construct the input tweet directly. This model outperforms competitive models that work on word-level like PV-DM, PV-DBOW on semantic similarity computation and sentiment classification tasks, thereby showing that the character-level nature of Tweet2Vec is best-suited to deal with the noise and idiosyncrasies of tweets. Tweet2Vec controls the generalization error by using a data augmentation technique, wherein tweets are replicated and some of the words in the replicated tweets are replaced with their synonyms. Both SDAE and Tweet2Vec has the advantage that they don't need a coherent inter-sentence narrative (like STV), which is hard to obtain in Twitter. Modeling using weak supervision Motivation: In a weakly supervised setup, we create labels for a tweet automatically and predict them to learn potentially sophisticated models than those obtained by unsupervised learning alone. Examples of labels include sentiment of the overall tweet, words like hashtag present in the tweet and so on. This technique can create a huge labeled dataset especially for building data-hungry, sophisticated deep learning models. Models: BIBREF26 learns sentiment-specific word embedding (SSWE), which encodes the polarity information in the word representations so that words with contrasting polarities and similar syntactic context (like good and bad) are pushed away from each other in the semantic space that it learns. SSWE utilizes the massive distant-supervised tweets collected by positive and negative emoticons to build a powerful tweet representation, which are shown to be useful in tasks such as sentiment classification and word similarity computation in sentiment lexicon. BIBREF2 observes that hashtags in tweets can be considered as topics and hence tweets with similar hashtags must come closer to each other. Their model predicts the hashtags by using a Bi-GRU layer to embed the tweets from its characters. Due to subword modeling, such character-level models can approximate the representations for rare words and new words (words not seen during training) in the test tweets really well. This model outperforms the word-level baselines for hashtag prediction task, thereby concluding that exploring character-level models for tweets is a worthy research direction to pursue. Both these works fail to study the model's generality BIBREF27 , i.e., the ability of the model to transfer the learned representations to diverse tasks. Future Directions In this section we present the future research directions which we believe can be worth pursuing to generate high quality tweet embeddings. Conclusion In this work we study the problem of learning unsupervised tweet representations. We believe our survey of the existing works based on the objective function can give vital perspectives to researchers and aid their understanding of the field. We also believe the future research directions studied in this work can help in breaking the barriers in building high quality, general purpose tweet representation models.
Do they evaluate their learned representations on downstream tasks?
2c7494d47b2a69f182e83455fe4c75ae3b2893e9
Do Ġthey Ġevaluate Ġtheir Ġlearned Ġrepresentations Ġon Ġdownstream Ġtasks ?
No
<s> Do Ġthey Ġevaluate Ġtheir Ġlearned Ġrepresentations Ġon Ġdownstream Ġtasks ? </s> Introduction </s> Twitter Ġis Ġa Ġwidely Ġused Ġmicro blog ging Ġplatform , Ġwhere Ġusers Ġpost Ġand Ġinteract Ġwith Ġmessages , ĠâĢ ľ t we ets âĢ Ŀ . ĠUnderstanding Ġthe Ġsemantic Ġrepresentation Ġof Ġtweets Ġcan Ġbenefit Ġa Ġplethora Ġof Ġapplications Ġsuch Ġas Ġsentiment Ġanalysis ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, Ġhashtag Ġprediction ĠB IB REF 2 Ġ, Ġparaph rase Ġdetection ĠB IB REF 3 Ġand Ġmicro blog Ġranking ĠB IB REF 4 Ġ, ĠB IB REF 5 Ġ. ĠHowever , Ġtweets Ġare Ġdifficult Ġto Ġmodel Ġas Ġthey Ġpose Ġseveral Ġchallenges Ġsuch Ġas Ġshort Ġlength , Ġinformal Ġwords , Ġunusual Ġgrammar Ġand Ġmiss pell ings . ĠRecently , Ġresearchers Ġare Ġfocusing Ġon Ġleveraging Ġun super vised Ġrepresentation Ġlearning Ġmethods Ġbased Ġon Ġneural Ġnetworks Ġto Ġsolve Ġthis Ġproblem . ĠOnce Ġthese Ġrepresentations Ġare Ġlearned , Ġwe Ġcan Ġuse Ġoff - the - she lf Ġpredict ors Ġtaking Ġthe Ġrepresentation Ġas Ġinput Ġto Ġsolve Ġthe Ġdownstream Ġtask ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ. ĠThese Ġmethods Ġenjoy Ġseveral Ġadvantages : Ġ( 1 ) Ġthey Ġare Ġcheaper Ġto Ġtrain , Ġas Ġthey Ġwork Ġwith Ġunl ab elled Ġdata , Ġ( 2 ) Ġthey Ġreduce Ġthe Ġdependence Ġon Ġdomain Ġlevel Ġexperts , Ġand Ġ( 3 ) Ġthey Ġare Ġhighly Ġeffective Ġacross Ġmultiple Ġapplications , Ġin Ġpractice . </s> Despite Ġthis , Ġthere Ġis Ġa Ġlack Ġof Ġprior Ġwork Ġwhich Ġsurveys Ġthe Ġtweet - specific Ġun super vised Ġrepresentation Ġlearning Ġmodels . ĠIn Ġthis Ġwork , Ġwe Ġattempt Ġto Ġfill Ġthis Ġgap Ġby Ġinvestigating Ġthe Ġmodels Ġin Ġan Ġorganized Ġfashion . ĠSpecifically , Ġwe Ġgroup Ġthe Ġmodels Ġbased Ġon Ġthe Ġobjective Ġfunction Ġit Ġoptim izes . ĠWe Ġbelieve Ġthis Ġwork Ġcan Ġaid Ġthe Ġunderstanding Ġof Ġthe Ġexisting Ġliterature . ĠWe Ġconclude Ġthe Ġpaper Ġby Ġpresenting Ġinteresting Ġfuture Ġresearch Ġdirections , Ġwhich Ġwe Ġbelieve Ġare Ġfruitful Ġin Ġadvancing Ġthis Ġfield Ġby Ġbuilding Ġhigh - quality Ġtweet Ġrepresentation Ġlearning Ġmodels . </s> Un super vised ĠTweet ĠRepresent ation ĠModels </s> There Ġare Ġvarious Ġmodels Ġspanning Ġacross Ġdifferent Ġmodel Ġarchitectures Ġand Ġobjective Ġfunctions Ġin Ġthe Ġliterature Ġto Ġcompute Ġtweet Ġrepresentation Ġin Ġan Ġun super vised Ġfashion . ĠThese Ġmodels Ġwork Ġin Ġa Ġsemi - super vised Ġway Ġ- Ġthe Ġrepresentations Ġgenerated Ġby Ġthe Ġmodel Ġis Ġfed Ġto Ġan Ġoff - the - she lf Ġpredictor Ġlike ĠSupport ĠVector ĠMachines Ġ( S VM ) Ġto Ġsolve Ġa Ġparticular Ġdownstream Ġtask . ĠThese Ġmodels Ġspan Ġacross Ġa Ġwide Ġvariety Ġof Ġneural Ġnetwork Ġbased Ġarchitectures Ġincluding Ġaverage Ġof Ġword Ġvectors , Ġconv olution al - based , Ġrecurrent - based Ġand Ġso Ġon . ĠWe Ġbelieve Ġthat Ġthe Ġperformance Ġof Ġthese Ġmodels Ġis Ġhighly Ġdependent Ġon Ġthe Ġobjective Ġfunction Ġit Ġoptim izes ĠâĢĵ Ġpredicting Ġadjacent Ġword Ġ( within - t weet Ġrelationships ), Ġadjacent Ġtweet Ġ( inter - t weet Ġrelationships ), Ġthe Ġtweet Ġitself Ġ( aut oen c oder ), Ġmodeling Ġfrom Ġstructured Ġresources Ġlike Ġparaph rase Ġdatabases Ġand Ġweak Ġsupervision . ĠIn Ġthis Ġsection , Ġwe Ġprovide Ġthe Ġfirst Ġof Ġits Ġkind Ġsurvey Ġof Ġthe Ġrecent Ġtweet - specific Ġun super vised Ġmodels Ġin Ġan Ġorganized Ġfashion Ġto Ġunderstand Ġthe Ġliterature . ĠSpecifically , Ġwe Ġcategor ize Ġeach Ġmodel Ġbased Ġon Ġthe Ġoptimized Ġobjective Ġfunction Ġas Ġshown Ġin ĠFigure ĠFIG REF 1 Ġ. ĠNext , Ġwe Ġstudy Ġeach Ġcategory Ġone Ġby Ġone . </s> Mod eling Ġwithin - t weet Ġrelationships </s> Mot ivation : ĠEvery Ġtweet Ġis Ġassumed Ġto Ġhave Ġa Ġlatent Ġtopic Ġvector , Ġwhich Ġinfluences Ġthe Ġdistribution Ġof Ġthe Ġwords Ġin Ġthe Ġtweet . ĠFor Ġexample , Ġthough Ġthe Ġappearance Ġof Ġthe Ġphrase Ġcatch Ġthe Ġball Ġis Ġfrequent Ġin Ġthe Ġcorpus , Ġif Ġwe Ġknow Ġthat Ġthe Ġtopic Ġof Ġa Ġtweet Ġis Ġabout ĠâĢ ľ technology âĢ Ŀ , Ġwe Ġcan Ġexpect Ġwords Ġsuch Ġas Ġbug Ġor Ġexception Ġafter Ġthe Ġword Ġcatch Ġ( ign oring Ġthe ) Ġinstead Ġof Ġthe Ġword Ġball Ġsince Ġcatch Ġthe Ġbug / ex ception Ġis Ġmore Ġplausible Ġunder Ġthe Ġtopic ĠâĢ ľ technology âĢ Ŀ . ĠOn Ġthe Ġother Ġhand , Ġif Ġthe Ġtopic Ġof Ġthe Ġtweet Ġis Ġabout ĠâĢ ľ sports âĢ Ŀ , Ġthen Ġwe Ġcan Ġexpect Ġball Ġafter Ġcatch . ĠThese Ġintu itions Ġindicate Ġthat Ġthe Ġprediction Ġof Ġneighboring Ġwords Ġfor Ġa Ġgiven Ġword Ġstrongly Ġrelies Ġon Ġthe Ġtweet Ġalso . </s> Mod els : ĠB IB REF 8 Ġ' s Ġwork Ġis Ġthe Ġfirst Ġto Ġexploit Ġthis Ġidea Ġto Ġcompute Ġdistributed Ġdocument Ġrepresentations Ġthat Ġare Ġgood Ġat Ġpredicting Ġwords Ġin Ġthe Ġdocument . ĠThey Ġpropose Ġtwo Ġmodels : ĠPV - DM Ġand ĠPV - DB OW , Ġthat Ġare Ġextensions Ġof ĠContinuous ĠBag ĠOf ĠWords Ġ( CB OW ) Ġand ĠSkip - gram Ġmodel Ġvariants Ġof Ġthe Ġpopular ĠWord 2 V ec Ġmodel ĠB IB REF 9 Ġrespectively ĠâĢĵ ĠPV - DM Ġinserts Ġan Ġadditional Ġdocument Ġtoken Ġ( which Ġcan Ġbe Ġthought Ġof Ġas Ġanother Ġword ) Ġwhich Ġis Ġshared Ġacross Ġall Ġcontexts Ġgenerated Ġfrom Ġthe Ġsame Ġdocument ; ĠPV - DB OW Ġattempts Ġto Ġpredict Ġthe Ġsampled Ġwords Ġfrom Ġthe Ġdocument Ġgiven Ġthe Ġdocument Ġrepresentation . ĠAlthough Ġoriginally Ġemployed Ġfor Ġparagraphs Ġand Ġdocuments , Ġthese Ġmodels Ġwork Ġbetter Ġthan Ġthe Ġtraditional Ġmodels : ĠB OW ĠB IB REF 10 Ġand ĠL DA ĠB IB REF 11 Ġfor Ġtweet Ġclassification Ġand Ġmicro blog Ġretrieval Ġtasks ĠB IB REF 12 Ġ. ĠThe Ġauthors Ġin ĠB IB REF 12 Ġmake Ġthe ĠPV - DM Ġand ĠPV - DB OW Ġmodels Ġconcept - aware Ġ( a Ġrich Ġsemantic Ġsignal Ġfrom Ġa Ġtweet ) Ġby Ġaugment ing Ġtwo Ġfeatures : Ġattention Ġover Ġcontextual Ġwords Ġand Ġconceptual Ġtweet Ġembed ding , Ġwhich Ġjointly Ġexploit Ġconcept - level Ġsenses Ġof Ġtweets Ġto Ġcompute Ġbetter Ġrepresentations . ĠBoth Ġthe Ġdiscussed Ġworks Ġhave Ġthe Ġfollowing Ġcharacteristics : Ġ( 1 ) Ġthey Ġuse Ġa Ġshallow Ġarchitecture , Ġwhich Ġenables Ġfast Ġtraining , Ġ( 2 ) Ġcomputing Ġrepresentations Ġfor Ġtest Ġtweets Ġrequires Ġcomputing Ġgrad ients , Ġwhich Ġis Ġtime - consuming Ġfor Ġreal - time ĠTwitter Ġapplications , Ġand Ġ( 3 ) Ġmost Ġimportantly , Ġthey Ġfail Ġto Ġexploit Ġtextual Ġinformation Ġfrom Ġrelated Ġtweets Ġthat Ġcan Ġbear Ġsalient Ġsemantic Ġsignals . </s> Mod eling Ġinter - t weet Ġrelationships </s> Mot ivation : ĠTo Ġcapture Ġrich Ġtweet Ġsemantics , Ġresearchers Ġare Ġattempting Ġto Ġexploit Ġa Ġtype Ġof Ġsentence - level ĠDistribution al ĠHyp ot hesis ĠB IB REF 10 Ġ, ĠB IB REF 13 Ġ. ĠThe Ġidea Ġis Ġto Ġinfer Ġthe Ġtweet Ġrepresentation Ġfrom Ġthe Ġcontent Ġof Ġadjacent Ġtweets Ġin Ġa Ġrelated Ġstream Ġlike Ġusers ' ĠTwitter Ġtimeline , Ġtopical , Ġretweet Ġand Ġconvers ational Ġstream . ĠThis Ġapproach Ġsignificantly Ġallev iates Ġthe Ġcontext Ġins uff iciency Ġproblem Ġcaused Ġdue Ġto Ġthe Ġambiguous Ġand Ġshort Ġnature Ġof Ġtweets ĠB IB REF 0 Ġ, ĠB IB REF 14 Ġ. </s> Mod els : ĠSkip - thought Ġvectors ĠB IB REF 15 Ġ( ST V ) Ġis Ġa Ġwidely Ġpopular Ġsentence Ġenc oder , Ġwhich Ġis Ġtrained Ġto Ġpredict Ġadjacent Ġsentences Ġin Ġthe Ġbook Ġcorpus ĠB IB REF 16 Ġ. ĠAlthough Ġthe Ġtesting Ġis Ġcheap Ġas Ġit Ġinvolves Ġa Ġcheap Ġforward Ġpropagation Ġof Ġthe Ġtest Ġsentence , ĠST V Ġis Ġvery Ġslow Ġto Ġtrain Ġthanks Ġto Ġits Ġcomplicated Ġmodel Ġarchitecture . ĠTo Ġcombat Ġthis Ġcomputational Ġin efficiency , ĠFast Sent ĠB IB REF 17 Ġpropose Ġa Ġsimple Ġadditive Ġ( log - linear ) Ġsentence Ġmodel , Ġwhich Ġpredicts Ġadjacent Ġsentences Ġ( represented Ġas ĠB OW ) Ġtaking Ġthe ĠB OW Ġrepresentation Ġof Ġsome Ġsentence Ġin Ġcontext . ĠThis Ġmodel Ġcan Ġexploit Ġthe Ġsame Ġsignal , Ġbut Ġat Ġa Ġmuch Ġlower Ġcomputational Ġexpense . ĠParallel Ġto Ġthis Ġwork , ĠS iam ase ĠCB OW ĠB IB REF 18 Ġdevelop Ġa Ġmodel Ġwhich Ġdirectly Ġcompares Ġthe ĠB OW Ġrepresentation Ġof Ġtwo Ġsentence Ġto Ġbring Ġthe Ġembed d ings Ġof Ġa Ġsentence Ġcloser Ġto Ġits Ġadjacent Ġsentence , Ġaway Ġfrom Ġa Ġrandomly Ġoccurring Ġsentence Ġin Ġthe Ġcorpus . ĠFor ĠFast Sent Ġand ĠSi ames e ĠCB OW , Ġthe Ġtest Ġsentence Ġrepresentation Ġis Ġa Ġsimple Ġaverage Ġof Ġword Ġvectors Ġobtained Ġafter Ġtraining . ĠBoth Ġof Ġthese Ġmodels Ġare Ġgeneral Ġpurpose Ġsentence Ġrepresentation Ġmodels Ġtrained Ġon Ġbook Ġcorpus , Ġyet Ġgive Ġa Ġcompetitive Ġperformance Ġover Ġprevious Ġmodels Ġon Ġthe Ġtweet Ġsemantic Ġsimilarity Ġcomputation Ġtask . ĠB IB REF 14 Ġ' s Ġmodel Ġattempt Ġto Ġexploit Ġthese Ġsignals Ġdirectly Ġfrom ĠTwitter . ĠWith Ġthe Ġhelp Ġof Ġattention Ġtechnique Ġand Ġlearned Ġuser Ġrepresentation , Ġthis Ġlog - linear Ġmodel Ġis Ġable Ġto Ġcapture Ġsalient Ġsemantic Ġinformation Ġfrom Ġchron ologically Ġadjacent Ġtweets Ġof Ġa Ġtarget Ġtweet Ġin Ġusers ' ĠTwitter Ġtimeline . </s> Mod eling Ġfrom Ġstructured Ġresources </s> Mot ivation : ĠIn Ġrecent Ġtimes , Ġbuilding Ġrepresentation Ġmodels Ġbased Ġon Ġsupervision Ġfrom Ġrich ly Ġstructured Ġresources Ġsuch Ġas ĠPar aph rase ĠDatabase Ġ( P PD B ) ĠB IB REF 19 Ġ( containing Ġnoisy Ġphrase Ġpairs ) Ġhas Ġyielded Ġhigh Ġquality Ġsentence Ġrepresentations . ĠThese Ġmethods Ġwork Ġby Ġmaximizing Ġthe Ġsimilarity Ġof Ġthe Ġsentences Ġin Ġthe Ġlearned Ġsemantic Ġspace . </s> Mod els : ĠCHAR AG RAM ĠB IB REF 20 Ġembed s Ġtextual Ġsequences Ġby Ġlearning Ġa Ġcharacter - based Ġcompos itional Ġmodel Ġthat Ġinvolves Ġaddition Ġof Ġthe Ġvectors Ġof Ġits Ġcharacter Ġn - gram s Ġfollowed Ġby Ġan Ġelement wise Ġnon linear ity . ĠThis Ġsimpler Ġarchitecture Ġtrained Ġon ĠP PD B Ġis Ġable Ġto Ġbeat Ġmodels Ġwith Ġcomplex Ġarchitectures Ġlike ĠCNN , ĠL ST M Ġon ĠSem E val Ġ2015 ĠTwitter Ġtextual Ġsimilarity Ġtask Ġby Ġa Ġlarge Ġmargin . ĠThis Ġresult Ġemphasizes Ġthe Ġimportance Ġof Ġcharacter - level Ġmodels Ġthat Ġaddress Ġdifferences Ġdue Ġto Ġspelling Ġvariation Ġand Ġword Ġchoice . ĠThe Ġauthors Ġin Ġtheir Ġsubsequent Ġwork ĠB IB REF 21 Ġconduct Ġa Ġcomprehensive Ġanalysis Ġof Ġmodels Ġspanning Ġthe Ġrange Ġof Ġcomplexity Ġfrom Ġword Ġaveraging Ġto ĠL ST Ms Ġfor Ġits Ġability Ġto Ġdo Ġtransfer Ġand Ġsupervised Ġlearning Ġafter Ġoptimizing Ġa Ġmargin Ġbased Ġloss Ġon ĠP PD B . ĠFor Ġtransfer Ġlearning , Ġthey Ġfind Ġmodels Ġbased Ġon Ġword Ġaveraging Ġperform Ġwell Ġon Ġboth Ġthe Ġin - domain Ġand Ġout - of - domain Ġtextual Ġsimilarity Ġtasks , Ġbeating ĠL ST M Ġmodel Ġby Ġa Ġlarge Ġmargin . ĠOn Ġthe Ġother Ġhand , Ġthe Ġword Ġaveraging Ġmodels Ġperform Ġwell Ġfor Ġboth Ġsentence Ġsimilarity Ġand Ġtextual Ġentail ment Ġtasks , Ġoutper forming Ġthe ĠL ST M . ĠHowever , Ġfor Ġsentiment Ġclassification Ġtask , Ġthey Ġfind ĠL ST M Ġ( trained Ġon ĠP PD B ) Ġto Ġbeat Ġthe Ġaveraging Ġmodels Ġto Ġestablish Ġa Ġnew Ġstate Ġof Ġthe Ġart . ĠThe Ġabove Ġresults Ġsuggest Ġthat Ġstructured Ġresources Ġplay Ġa Ġvital Ġrole Ġin Ġcomputing Ġgeneral - purpose Ġembed d ings Ġuseful Ġin Ġdownstream Ġapplications . </s> Mod eling Ġas Ġan Ġaut oen c oder </s> Mot ivation : ĠThe Ġaut oen c oder Ġbased Ġapproach Ġlearns Ġlatent Ġ( or Ġcompressed ) Ġrepresentation Ġby Ġreconstruct ing Ġits Ġown Ġinput . ĠSince Ġtextual Ġdata Ġlike Ġtweets Ġcontain Ġdiscrete Ġinput Ġsignals , Ġsequence - to - sequence Ġmodels ĠB IB REF 22 Ġlike ĠST V Ġcan Ġbe Ġused Ġto Ġbuild Ġthe Ġsolution . ĠThe Ġenc oder Ġmodel Ġwhich Ġenc odes Ġthe Ġinput Ġtweet Ġcan Ġtypically Ġbe Ġa ĠCNN ĠB IB REF 23 Ġ, Ġrecurrent Ġmodels Ġlike ĠR NN , ĠGR U , ĠL ST M ĠB IB REF 24 Ġor Ġmemory Ġnetworks ĠB IB REF 25 Ġ. ĠThe Ġdec oder Ġmodel Ġwhich Ġgenerates Ġthe Ġoutput Ġtweet Ġcan Ġtypically Ġbe Ġa Ġrecurrent Ġmodel Ġthat Ġpredicts Ġa Ġoutput Ġtoken Ġat Ġevery Ġtime Ġstep . </s> Mod els : ĠSequ ential ĠDen o ising ĠAut oen cod ers Ġ( S DA E ) ĠB IB REF 17 Ġis Ġa ĠL ST M - based Ġsequence - to - sequence Ġmodel , Ġwhich Ġis Ġtrained Ġto Ġrecover Ġthe Ġoriginal Ġdata Ġfrom Ġthe Ġcorrupted Ġversion . ĠS DA E Ġproduces Ġrobust Ġrepresentations Ġby Ġlearning Ġto Ġrepresent Ġthe Ġdata Ġin Ġterms Ġof Ġfeatures Ġthat Ġexplain Ġits Ġimportant Ġfactors Ġof Ġvariation . ĠTweet 2 V ec ĠB IB REF 3 Ġis Ġa Ġrecent Ġmodel Ġwhich Ġuses Ġa Ġcharacter - level ĠCNN - L ST M Ġenc oder - dec oder Ġarchitecture Ġtrained Ġto Ġconstruct Ġthe Ġinput Ġtweet Ġdirectly . ĠThis Ġmodel Ġoutper forms Ġcompetitive Ġmodels Ġthat Ġwork Ġon Ġword - level Ġlike ĠPV - DM , ĠPV - DB OW Ġon Ġsemantic Ġsimilarity Ġcomputation Ġand Ġsentiment Ġclassification Ġtasks , Ġthereby Ġshowing Ġthat Ġthe Ġcharacter - level Ġnature Ġof ĠTweet 2 V ec Ġis Ġbest - su ited Ġto Ġdeal Ġwith Ġthe Ġnoise Ġand Ġidiosyncr as ies Ġof Ġtweets . ĠTweet 2 V ec Ġcontrols Ġthe Ġgeneral ization Ġerror Ġby Ġusing Ġa Ġdata Ġaug mentation Ġtechnique , Ġwherein Ġtweets Ġare Ġreplicated Ġand Ġsome Ġof Ġthe Ġwords Ġin Ġthe Ġreplicated Ġtweets Ġare Ġreplaced Ġwith Ġtheir Ġsyn onyms . ĠBoth ĠS DA E Ġand ĠTweet 2 V ec Ġhas Ġthe Ġadvantage Ġthat Ġthey Ġdon 't Ġneed Ġa Ġcoherent Ġinter - sent ence Ġnarrative Ġ( like ĠST V ), Ġwhich Ġis Ġhard Ġto Ġobtain Ġin ĠTwitter . </s> Mod eling Ġusing Ġweak Ġsupervision </s> Mot ivation : ĠIn Ġa Ġweak ly Ġsupervised Ġsetup , Ġwe Ġcreate Ġlabels Ġfor Ġa Ġtweet Ġautomatically Ġand Ġpredict Ġthem Ġto Ġlearn Ġpotentially Ġsophisticated Ġmodels Ġthan Ġthose Ġobtained Ġby Ġun super vised Ġlearning Ġalone . ĠExamples Ġof Ġlabels Ġinclude Ġsentiment Ġof Ġthe Ġoverall Ġtweet , Ġwords Ġlike Ġhashtag Ġpresent Ġin Ġthe Ġtweet Ġand Ġso Ġon . ĠThis Ġtechnique Ġcan Ġcreate Ġa Ġhuge Ġlabeled Ġdataset Ġespecially Ġfor Ġbuilding Ġdata - hung ry , Ġsophisticated Ġdeep Ġlearning Ġmodels . </s> Mod els : ĠB IB REF 26 Ġlearns Ġsentiment - specific Ġword Ġembed ding Ġ( SS WE ), Ġwhich Ġenc odes Ġthe Ġpol arity Ġinformation Ġin Ġthe Ġword Ġrepresentations Ġso Ġthat Ġwords Ġwith Ġcontrasting Ġpolar ities Ġand Ġsimilar Ġsynt actic Ġcontext Ġ( like Ġgood Ġand Ġbad ) Ġare Ġpushed Ġaway Ġfrom Ġeach Ġother Ġin Ġthe Ġsemantic Ġspace Ġthat Ġit Ġlearns . ĠSS WE Ġutilizes Ġthe Ġmassive Ġdistant - super vised Ġtweets Ġcollected Ġby Ġpositive Ġand Ġnegative Ġemot icons Ġto Ġbuild Ġa Ġpowerful Ġtweet Ġrepresentation , Ġwhich Ġare Ġshown Ġto Ġbe Ġuseful Ġin Ġtasks Ġsuch Ġas Ġsentiment Ġclassification Ġand Ġword Ġsimilarity Ġcomputation Ġin Ġsentiment Ġlex icon . ĠB IB REF 2 Ġobserves Ġthat Ġhasht ags Ġin Ġtweets Ġcan Ġbe Ġconsidered Ġas Ġtopics Ġand Ġhence Ġtweets Ġwith Ġsimilar Ġhasht ags Ġmust Ġcome Ġcloser Ġto Ġeach Ġother . ĠTheir Ġmodel Ġpredicts Ġthe Ġhasht ags Ġby Ġusing Ġa ĠBi - GR U Ġlayer Ġto Ġembed Ġthe Ġtweets Ġfrom Ġits Ġcharacters . ĠDue Ġto Ġsub word Ġmodeling , Ġsuch Ġcharacter - level Ġmodels Ġcan Ġapproximate Ġthe Ġrepresentations Ġfor Ġrare Ġwords Ġand Ġnew Ġwords Ġ( words Ġnot Ġseen Ġduring Ġtraining ) Ġin Ġthe Ġtest Ġtweets Ġreally Ġwell . ĠThis Ġmodel Ġoutper forms Ġthe Ġword - level Ġbas elines Ġfor Ġhashtag Ġprediction Ġtask , Ġthereby Ġconcluding Ġthat Ġexploring Ġcharacter - level Ġmodels Ġfor Ġtweets Ġis Ġa Ġworthy Ġresearch Ġdirection Ġto Ġpursue . ĠBoth Ġthese Ġworks Ġfail Ġto Ġstudy Ġthe Ġmodel 's Ġgener ality ĠB IB REF 27 Ġ, Ġi . e ., Ġthe Ġability Ġof Ġthe Ġmodel Ġto Ġtransfer Ġthe Ġlearned Ġrepresentations Ġto Ġdiverse Ġtasks . </s> Future ĠDirections </s> In Ġthis Ġsection Ġwe Ġpresent Ġthe Ġfuture Ġresearch Ġdirections Ġwhich Ġwe Ġbelieve Ġcan Ġbe Ġworth Ġpursuing Ġto Ġgenerate Ġhigh Ġquality Ġtweet Ġembed d ings . </s> Conclusion </s> In Ġthis Ġwork Ġwe Ġstudy Ġthe Ġproblem Ġof Ġlearning Ġun super vised Ġtweet Ġrepresentations . ĠWe Ġbelieve Ġour Ġsurvey Ġof Ġthe Ġexisting Ġworks Ġbased Ġon Ġthe Ġobjective Ġfunction Ġcan Ġgive Ġvital Ġperspectives Ġto Ġresearchers Ġand Ġaid Ġtheir Ġunderstanding Ġof Ġthe Ġfield . ĠWe Ġalso Ġbelieve Ġthe Ġfuture Ġresearch Ġdirections Ġstudied Ġin Ġthis Ġwork Ġcan Ġhelp Ġin Ġbreaking Ġthe Ġbarriers Ġin Ġbuilding Ġhigh Ġquality , Ġgeneral Ġpurpose Ġtweet Ġrepresentation Ġmodels .