1500 sentences
EXTRACTIVE
1500 sentences
EXTRACTIVE
A parser model was trained for Hindi using the MSTParser BIBREF2 by a part of the the Hindi Dependency Treebank data (18708 sentences) from IIIT-Hyderabad BIBREF3 . A part of the Penn Treebank (28188 sentences) was used for training an English parser BIBREF4 . The treebanks were converted to MSTParser format from ConLL format for training. A part of the ILCI English-Hindi Tourism parallel corpus (1500 sentences) was used for training the classifiers. This corpus was POS-tagged using the Stanford POS Tagger BIBREF5 for English and using the Hindi POS Tagger BIBREF6 from IIIT-Hyderabad for Hindi. It was then automatically annotated with dependency parse trees by the parsers we had trained before English and Hindi.
A parser model was trained for Hindi using the MSTParser BIBREF2 by a part of the the Hindi Dependency Treebank data (18708 sentences) from IIIT-Hyderabad BIBREF3 . A part of the Penn Treebank (28188 sentences) was used for training an English parser BIBREF4 . The treebanks were converted to MSTParser format from ConLL format for training. A part of the ILCI English-Hindi Tourism parallel corpus (1500 sentences) was used for training the classifiers. This corpus was POS-tagged using the Stanford POS Tagger BIBREF5 for English and using the Hindi POS Tagger BIBREF6 from IIIT-Hyderabad for Hindi. It was then automatically annotated with dependency parse trees by the parsers we had trained before English and Hindi.
1603.08594
Introduction </s> Pre pos itional ĠPh rase Ġ( PP ) Ġattachment Ġdis amb ig uation Ġis Ġan Ġimportant Ġproblem Ġin ĠN LP , Ġfor Ġit Ġoften Ġgives Ġrise Ġto Ġincorrect Ġparse Ġtrees Ġ. ĠStatistical Ġpars ers Ġoften Ġpredict Ġincorrect Ġattachment Ġfor Ġpre pos itional Ġphrases . ĠFor Ġapplications Ġlike ĠMachine ĠTranslation , Ġincorrect ĠPP - att achment Ġleads Ġto Ġserious Ġerrors Ġin Ġtranslation . ĠSeveral Ġapproaches Ġhave Ġbeen Ġproposed Ġto Ġsolve Ġthis Ġproblem . ĠWe Ġattempt Ġto Ġtackle Ġthis Ġproblem Ġfor ĠEnglish . ĠEnglish Ġis Ġa Ġsynt act ically Ġambiguous Ġlanguage Ġwith Ġrespect Ġto ĠPP Ġattachments . ĠFor Ġexample , Ġconsider Ġthe Ġfollowing Ġsentence Ġwhere Ġthe Ġpre pos itional Ġphrase Ġwith Ġpockets Ġmay Ġattach Ġeither Ġto Ġthe Ġverb Ġwashed Ġor Ġto Ġthe Ġnoun Ġjeans . </s> Sent ence ĠIN LINE FORM 0 Ġ: ĠI Ġwashed Ġthe Ġjeans Ġwith Ġpockets . </s> Below Ġis Ġthe Ġcorrect Ġdependency Ġparse Ġtree Ġ( for Ġsentence Ġ1 ) Ġwhere Ġthe Ġpre pos itional Ġphrase Ġwith Ġpockets Ġis Ġattached Ġto Ġthe Ġnoun Ġjeans . </s> Another Ġpossible Ġparse Ġtree Ġfor Ġthe Ġsame Ġsentence Ġcould Ġbe Ġas Ġshown Ġin ĠFigure ĠFIG REF 2 Ġ: </s> A Ġstatistical Ġparser Ġoften Ġpredicts Ġthe ĠPP - att achment Ġincorrectly , Ġand Ġmay Ġlead Ġto Ġincorrect Ġparse Ġtrees . ĠLet Ġus Ġnow Ġlook Ġat Ġanother Ġsentence . </s> Sent ence ĠIN LINE FORM 0 Ġ: ĠI Ġwashed Ġthe Ġjeans Ġwith Ġsoap . </s> The Ġcorrect Ġdependency Ġtree Ġfor Ġsentence ĠIN LINE FORM 0 Ġis Ġthe Ġfollowing Ġ( Figure ĠFIG REF 3 Ġ), Ġwhere Ġthe Ġpre pos itional Ġphrase Ġwith Ġsoap Ġattaches Ġto Ġthe Ġverb Ġwashed . </s> Clearly , Ġthere Ġis Ġa Ġcase Ġof Ġambiguity Ġthat Ġcan Ġbe Ġresolved Ġonly Ġif Ġthe Ġsemantics Ġare Ġknown . ĠIn Ġthis Ġcase , Ġthe Ġfact Ġthat Ġsoap Ġis Ġan Ġaid Ġto Ġthe Ġverb Ġwashed Ġdis amb ig uates Ġits Ġattachment Ġto Ġthe Ġverb Ġrather Ġthan Ġthe Ġnoun Ġjeans . ĠFor Ġcorrectly Ġtranslating Ġsuch Ġan ĠEnglish Ġsentence Ġto Ġanother Ġlanguage , Ġthe Ġattachments Ġneed Ġto Ġbe Ġmarked Ġcorrectly . </s> In Ġthis Ġwork , Ġwe Ġpropose Ġa ĠDual ĠDec om position Ġ( DD ) Ġbased Ġalgorithm Ġfor Ġsolving Ġthe ĠPP Ġattachment Ġproblem . ĠWe Ġtry Ġto Ġdis amb ig uate Ġthe ĠPP Ġattachments Ġfor ĠEnglish Ġusing Ġthe Ġcorresponding Ġparallel ĠHindi Ġcorpor a . ĠHindi Ġis Ġa Ġsynt act ically Ġrich Ġlanguage Ġand Ġin Ġmost Ġcases Ġexhibits Ġno Ġattachment Ġambig u ities . ĠThe Ġuse Ġof Ġcase Ġmarkers Ġand Ġthe Ġinherent Ġconstruction Ġof Ġsentences Ġin ĠHindi Ġmake Ġcases Ġof Ġambiguity Ġrare r . ĠLet Ġus Ġexamine Ġhow Ġsentences Ġ1 Ġand Ġ2 Ġwould Ġlook Ġlike Ġin ĠHindi , Ġand Ġif Ġthere Ġis Ġa Ġcase Ġfor Ġambiguity . </s> Sent ence Ġ( 3 ) Ġand Ġsentence Ġ( 4 ) Ġare Ġthe Ġrespective ĠHindi Ġtranslations Ġof Ġsentence Ġ( 1 ) Ġand Ġ( 2 )). </s> Sent ence ĠIN LINE FORM 0 Ġ: Ġm n Ġj b Ġv Al F Ġj F 06 ws ĠD oy F ĠIN LINE FORM 1 </s> Sent ence ĠIN LINE FORM 0 Ġ: Ġm n Ġs Ab n Ġs Ġj F 06 ws ĠD oy F ĠIN LINE FORM 1 </s> In Ġsentence Ġ( 3 ), Ġthe Ġpre pos itional Ġphrase Ġj eb Ġwa ali Ġattaches Ġto Ġthe Ġnoun Ġjeans Ġas Ġshown Ġin Ġthe Ġfigure ĠFIG REF 4 Ġ. </s> The Ġparse Ġtree Ġfor Ġsentence Ġ( 4 ) Ġis Ġshown Ġin Ġfigure ĠFIG REF 5 Ġ, Ġwhere Ġthe Ġpre pos itional Ġphrase Ġsa ab un Ġse Ġattaches Ġto Ġthe Ġverb Ġdh oy ee . </s> The Ġcase Ġmarkers Ġwa ali Ġand Ġse Ġin Ġthe Ġtwo Ġsentences Ġin ĠHindi Ġmake Ġthe Ġpp - at tt achment Ġclear . ĠIn Ġour Ġapproach , Ġwe Ġmake Ġuse Ġof Ġthe Ġparallel ĠHindi Ġsentences Ġto Ġdis amb ig uate Ġthe ĠPP Ġattachments Ġfor ĠEnglish Ġsentences . ĠThe Ġroadmap Ġof Ġthe Ġpaper Ġis Ġas Ġfollows : ĠWe Ġdiscuss Ġthe Ġliterature Ġand Ġrelated Ġwork Ġfor Ġsolving Ġthe ĠPP - att achment Ġproblem Ġin Ġsection Ġ[ ĠSEC REF 2 Ġ]. ĠSection Ġ[ ĠSEC REF 3 Ġ] Ġdescribes Ġour Ġapproach , Ġand Ġthe ĠDual ĠDec om position Ġalgorithm Ġin Ġdetail . ĠThe Ġsetup , Ġdata , Ġand Ġexperiments Ġare Ġcovered Ġin ĠSection Ġ[ ĠSEC REF 4 Ġ]. ĠWith ĠSection Ġ[ ĠSEC REF 5 Ġ], Ġwe Ġconclude Ġour Ġwork Ġand Ġdiscuss Ġscope Ġfor Ġfuture Ġwork . </s> Related ĠWork </s> A Ġnumber Ġof Ġsupervised Ġand Ġun super vised Ġapproaches Ġfor Ġsolving Ġthe ĠPP - att achment Ġproblem Ġhave Ġbeen Ġproposed Ġin Ġthe Ġliterature . ĠRat n ap ark hi : 94 Ġuse Ġa ĠMaximum ĠEnt ropy ĠModel Ġfor Ġsolving Ġthe ĠPP - att achment Ġdecision . ĠSchwartz : 03 Ġpropose Ġan Ġun super vised Ġapproach Ġfor Ġsolving ĠPP Ġattachment Ġusing Ġmult ilingual Ġaligned Ġdata . ĠThey Ġtransform Ġthe Ġdata Ġinto Ġhigh - level Ġlinguistic Ġrepresentations Ġand Ġuse Ġit Ġmake Ġre att achment Ġdecisions . ĠThe Ġintuition Ġis Ġsimilar Ġto Ġour Ġwork , Ġbut Ġthe Ġapproach Ġis Ġentirely Ġdifferent . ĠBrill : 94 Ġdiscuss Ġa Ġtransformation - based Ġrule Ġderiv ation Ġmethod Ġfor ĠPP - att achment Ġdis amb ig uation . ĠIt Ġis Ġa Ġsimple Ġlearning Ġalgorithm Ġwhich Ġderives Ġa Ġset Ġof Ġtransformation Ġrules Ġfrom Ġtraining Ġcorpus , Ġwhich Ġare Ġthen Ġused Ġfor Ġsolving Ġthe ĠPP - att achment Ġproblem . ĠSt et ina : 97 Ġmake Ġuse Ġof Ġthe Ġsemantic Ġdictionary Ġto Ġsolve Ġthe Ġproblem Ġof Ġdis amb ig uating ĠPP Ġattachments . ĠTheir Ġwork Ġdescribes Ġuse Ġof Ġword Ġsense Ġdis amb ig uation Ġ( W SD ) Ġfor Ġboth Ġsupervised Ġand Ġun super vised Ġtechniques . ĠAg ir re ĠAg ir re : 08 Ġand ĠMed imi ĠMed imi : 07 Ġhave Ġused ĠW SD - based Ġstrategies Ġin Ġdifferent Ġcapacities Ġto Ġsolve Ġthe Ġproblem Ġof ĠPP - att achment . ĠOl te anu : 05 Ġhave Ġattempted Ġto Ġsolve Ġthe Ġpp - att achment Ġproblem Ġas Ġa Ġclassification Ġproblem Ġof Ġattachment Ġeither Ġto Ġthe Ġpreceding Ġverb Ġor Ġthe Ġnoun , Ġand Ġhave Ġused ĠSupport ĠVector ĠMachines Ġ( S V Ms ) Ġthat Ġuse Ġcomplex Ġsynt actic Ġand Ġsemantic Ġfeatures . </s> Our ĠApproach </s> We Ġpropose Ġa ĠDual ĠDec om position Ġbased Ġinference Ġalgorithm Ġto Ġlook Ġat Ġthe Ġproblem Ġof ĠPP - att achment Ġdis amb ig uation . ĠDual Ġdecom position , Ġor Ġmore Ġgenerally , ĠLag rang ian ĠRelax ation , Ġis Ġa Ġclassical Ġmethod Ġfor Ġcomb inator ial Ġoptimization Ġand Ġhas Ġbeen Ġapplied Ġto Ġseveral Ġinference Ġproblems Ġin ĠN LP ĠB IB REF 0 Ġ. ĠWe Ġtrain Ġtwo Ġseparate Ġparser Ġmodels Ġfor ĠEnglish Ġand ĠHindi Ġeach , Ġusing Ġthe ĠM ST Parser , Ġand Ġmake Ġuse Ġof Ġthese Ġmodels Ġin Ġthe Ġinf eren cing Ġstep . ĠThe Ġinput Ġto Ġthe Ġalgorithm Ġis Ġa Ġparallel ĠEnglish - H ind i Ġsentence Ġpair , Ġwith Ġits Ġword Ġalign ments Ġgiven . ĠWe Ġfirst Ġobtain Ġthe Ġpredicted Ġparse Ġtrees Ġfor Ġthe ĠEnglish Ġand ĠHindi Ġsentences Ġfrom Ġthe Ġrespective Ġtrained Ġparser Ġmodels Ġas Ġan Ġinitials iation Ġstep . ĠThe ĠDD Ġalgorithm Ġthen Ġtries Ġto Ġenforce Ġagreement Ġbetween Ġthe Ġtwo Ġparse Ġtrees Ġsubject Ġto Ġthe Ġgiven Ġalign ments . </s> Let Ġus Ġtake Ġa Ġcloser Ġlook Ġat Ġwhat Ġwe Ġmean Ġby Ġagreement Ġbetween Ġthe Ġtwo Ġparse Ġtrees . ĠEssentially , Ġif Ġwe Ġhave Ġtwo Ġwords Ġin Ġthe ĠEnglish Ġsentence Ġden oted Ġby Ġi Ġand Ġi ', Ġaligned Ġto Ġwords Ġj Ġand Ġj ' Ġin Ġthe Ġparallel ĠHindi Ġsentence Ġrespectively , Ġwe Ġcan Ġexpect Ġa Ġdependency Ġedge Ġbetween Ġi Ġand Ġi ' Ġin Ġthe ĠEnglish Ġparse Ġtree Ġto Ġcorrespond Ġto Ġan Ġedge Ġbetween Ġj Ġand Ġj ' Ġin Ġthe ĠHindi Ġparse Ġtree , Ġand Ġvice Ġversa . ĠAlso , Ġin Ġorder Ġto Ġaccommodate Ġstructural Ġdiversity Ġin Ġlanguages ĠB IB REF 1 Ġ, Ġwe Ġcan Ġexpect Ġan Ġedge Ġin Ġthe Ġparse Ġtree Ġin Ġone Ġlanguage Ġto Ġcorrespond Ġto Ġmore Ġthan Ġone Ġedge , Ġor Ġrather , Ġa Ġpath , Ġin Ġthe Ġother Ġlanguage Ġparse Ġtree . ĠThis Ġhas Ġbeen Ġcaptured Ġin Ġthe Ġexamples Ġin Ġfigures ĠFIG REF 6 Ġ( A ) Ġand ĠFIG REF 6 Ġ( B ). ĠFor Ġan Ġedge Ġin Ġthe ĠEnglish Ġparse Ġtree , Ġwe Ġterm Ġthe Ġcorresponding Ġedge Ġor Ġpath Ġin Ġthe ĠHindi Ġparse Ġtree Ġas Ġthe Ġprojection Ġor Ġprojected Ġpath Ġof Ġthe ĠEnglish Ġedge Ġon Ġthe ĠHindi Ġparse Ġtree , Ġand Ġsimilarly Ġthere Ġare Ġprojected Ġpaths Ġfrom ĠHindi Ġto ĠEnglish . ĠFor Ġmatters Ġof Ġsimplicity , Ġwe Ġignore Ġthe Ġdirection Ġof Ġthe Ġedges Ġin Ġthe Ġparse Ġtrees . ĠThe Ġdual Ġdecom position Ġinference Ġalgorithm Ġtries Ġto Ġbring Ġthe Ġparse Ġtrees Ġin Ġthe Ġtwo Ġlanguages Ġthrough Ġits Ġconstraints . </s> The Ġproblem Ġis Ġformulated Ġas Ġbelow : </s> In Ġthe Ġabove Ġformulation , ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġrepresent Ġa ĠEnglish Ġand ĠHindi Ġsentence Ġrespectively . ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġare Ġthe Ġcorresponding Ġparse Ġtrees . ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġare Ġthe Ġmodel Ġparameters Ġfor Ġthe Ġedge - fact ored Ġparser Ġmodels Ġtrained Ġfor ĠEnglish Ġand ĠHindi Ġrespectively . ĠIN LINE FORM 6 Ġrepresents Ġan Ġedge Ġin Ġthe ĠEnglish Ġparse Ġtree ĠIN LINE FORM 7 Ġ. ĠIN LINE FORM 8 Ġis Ġa Ġprojected Ġpath Ġin ĠHindi Ġparse Ġtree ĠIN LINE FORM 9 Ġfor Ġa Ġgiven ĠEnglish Ġedge ĠIN LINE FORM 10 Ġ. ĠThe Ġterm ĠIN LINE FORM 11 Ġstands Ġfor Ġthe Ġscore Ġof Ġa Ġprojected Ġpath Ġin ĠHindi Ġparse Ġtree ĠIN LINE FORM 12 Ġfor Ġa Ġgiven ĠEnglish Ġedge ĠIN LINE FORM 13 Ġ. </s> The Ġscore Ġof Ġthe Ġprojected Ġpath Ġis Ġcalculated Ġas Ġthe Ġsum Ġof Ġscores Ġof Ġall Ġedges Ġin Ġthe Ġpath . ĠLet ĠIN LINE FORM 0 Ġdenote Ġthe Ġprojected Ġpath Ġon Ġsentence Ġh Ġin ĠHindi Ġfor Ġthe Ġedge ĠIN LINE FORM 1 Ġin Ġthe ĠEnglish Ġparse Ġtree . ĠWe Ġassume ĠIN LINE FORM 2 Ġwhere ĠIN LINE FORM 3 Ġis Ġthe Ġscore Ġof Ġedge Ġa Ġin Ġthe Ġprojected Ġpath ĠIN LINE FORM 4 Ġ. ĠIn Ġthe Ġother Ġdirection , ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġis Ġsimilarly Ġdefined . </s> To Ġsolve Ġthis Ġmaxim ization Ġproblem Ġin Ġfigure ĠFIG REF 7 Ġ, Ġwe Ġassume Ġone Ġtree Ġto Ġbe Ġgiven Ġand Ġmaximize Ġthe Ġother Ġand Ġthe Ġscore Ġof Ġits Ġprojected Ġpath . ĠThe Ġalgorithm Ġis Ġdescribed Ġin Ġdetail Ġin Ġsection ĠSEC REF 8 Ġ. </s> Dual ĠDec om position Ġbased ĠAl gorithm </s> We Ġuse Ġan Ġiter ative ĠCoord inate ĠDes cent Ġalgorithm Ġ( Al gorithm Ġ1 ) Ġwhich Ġcalls Ġthe ĠProject ĠAl gorithm Ġuntil Ġconvergence . ĠThe Ġtrees ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare Ġinitialized Ġby Ġthe Ġpreviously Ġtrained Ġparser Ġmodels Ġfor Ġthe Ġrespective Ġlanguages . </s> Co ordinate ĠDes cent ĠAl gorithm Ġ[ 1 ] ĠInitial ize ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġfrom Ġthe ĠM ST Parser Ġmodels Ġfor ĠIN LINE FORM 2 Ġto ĠIN LINE FORM 3 ĠIN LINE FORM 4 ĠIN LINE FORM 5 Ġif ĠIN LINE FORM 6 Ġor ĠIN LINE FORM 7 Ġbreak Ġelse ĠIN LINE FORM 8 ĠIN LINE FORM 9 Ġend Ġfor </s> For ĠIN LINE FORM 0 Ġiterations , Ġthe Ġproject Ġfunction Ġreturns Ġa Ġparse Ġtree Ġfor ĠEnglish Ġwhich Ġmaxim izes Ġthe Ġagreement Ġbetween Ġthe ĠEnglish Ġand ĠHindi Ġparse Ġtree Ġwhen Ġthe ĠHindi Ġparse Ġtree Ġis Ġfixed , Ġand Ġlikewise Ġfor Ġthe ĠHindi Ġparse Ġtree . ĠThe Ġalgorithm Ġconver ges Ġwhen Ġthe Ġtrees Ġno Ġlonger Ġchange , </s> Let Ġus Ġnow Ġlook Ġat Ġthe ĠProject Ġalgorithm Ġ( Al gorithm Ġ2 ) Ġin Ġdetail . ĠIt Ġpredicts Ġthe Ġtree Ġfor Ġa Ġsentence Ġin Ġthe Ġtarget Ġlanguage , Ġgiven Ġthe Ġparse Ġtree Ġin Ġthe Ġsource Ġlanguage , Ġand Ġthe Ġword Ġalign ments Ġbetween Ġthe Ġparallel Ġsentence . </s> Project ĠAl gorithm Ġ( tree ĠT , Ġsen ĠS ) Ġ[ 1 ] ĠA Ġparse Ġtree ĠT Ġ( H ind i ) Ġand Ġsentence ĠS Ġ( English ) ĠInitial ize ĠIN LINE FORM 0 ĠIN LINE FORM 1 Ġ= Ġ0 Ġfor ĠIN LINE FORM 2 Ġto ĠIN LINE FORM 3 ĠIN LINE FORM 4 Ġfor ĠIN LINE FORM 5 ĠIN LINE FORM 6 </s> IN LINE FORM 0 Ġend Ġfor Ġif ĠIN LINE FORM 1 Ġthen Ġreturn ĠIN LINE FORM 2 Ġelse ĠIN LINE FORM 3 ĠIN LINE FORM 4 Ġend Ġfor </s> The Ġlag rang ian Ġmultipl iers Ġare Ġinitialized Ġto Ġzero . ĠThe Ġbest Ġtree Ġin Ġthe Ġtarget Ġlanguage Ġis Ġpredicted Ġby Ġthe Ġarg max Ġcomputation Ġin Ġstep Ġ4 . ĠThis Ġmaxim ization Ġinvolves Ġthe Ġparser Ġmodel Ġparameters ĠIN LINE FORM 0 Ġand Ġthe Ġscore Ġof Ġthe Ġbest Ġprojected Ġpath Ġin Ġthe Ġsource Ġtree Ġfor Ġall Ġedges . ĠIN LINE FORM 1 Ġdenotes Ġthe Ġscore Ġof Ġthe Ġprojected Ġpath Ġof Ġthe Ġedge ĠIN LINE FORM 2 Ġon Ġthe Ġsource Ġtree ĠT . ĠIn Ġsteps Ġ6 Ġand Ġ7 , Ġthe Ġbest Ġprojected Ġpath Ġfor Ġevery Ġedge Ġof Ġthe Ġsource Ġtree Ġis Ġpredicted Ġon Ġthe Ġtarget Ġtree Ġusing Ġthe Ġclass ifiers Ġdescribed Ġin Ġsection Ġ. ĠThe Ġconstraints Ġhere Ġare Ġthat Ġthe Ġedges Ġin Ġthe Ġprojected Ġpaths Ġfrom Ġthe Ġclass ifiers Ġand Ġthe Ġpredicted Ġtrees Ġare Ġin Ġagreement . </s> Project ed ĠPath ĠPrediction </s> In Ġorder Ġto Ġpredict Ġthe Ġprojected Ġpath Ġin Ġone Ġlanguage Ġfor Ġan Ġedge Ġin Ġthe Ġother Ġlanguage , Ġwe Ġuse Ġa Ġset Ġof Ġtwo Ġclass ifiers Ġin Ġa Ġpipeline . ĠLet Ġus Ġrecall Ġthat Ġwe Ġhave Ġtwo Ġnodes Ġin Ġone Ġlanguage Ġwith Ġan Ġedge Ġbetween Ġthem , Ġand Ġwe Ġare Ġtrying Ġto Ġpredict Ġthe Ġpath Ġof Ġthe Ġcorresponding Ġaligned Ġnodes Ġin Ġthe Ġother Ġlanguage . ĠThe Ġfirst Ġclass ifier Ġpredicts Ġthe Ġlength Ġof Ġthe Ġprojected Ġpath , Ġand Ġthe Ġsecond Ġpredicts Ġthe Ġpredicted Ġpath Ġitself , Ġgiven Ġthe Ġpath Ġlength Ġfrom Ġthe Ġfirst Ġclass ifier . ĠLet Ġus Ġlook Ġat Ġthese Ġclass ifiers Ġseparately . </s> The Ġclass ifier Ġfor Ġpath Ġlength Ġprediction Ġis Ġa Ġset Ġof Ġfive Ġbinary Ġclass ifiers , Ġwhich Ġpredict Ġthe Ġpath Ġlength Ġto Ġbe Ġ1 , Ġ2 , Ġ3 , Ġ4 Ġor Ġ5 . ĠWe Ġassume Ġprojected Ġpath Ġlengths Ġto Ġbe Ġno Ġgreater Ġthan Ġ5 . ĠThese Ġclass ifiers Ġare Ġpercept rons Ġtrained Ġon Ġseparate Ġannot ated Ġdata . ĠThe Ġfeatures Ġused Ġwere Ġthe Ġwords Ġand ĠPOS Ġtags Ġof Ġthe Ġfour Ġnodes Ġin Ġthe Ġpair Ġof Ġalign ments Ġunder Ġconsideration . </s> The Ġclass ifier Ġfor Ġpath Ġprediction Ġis Ġa Ġset Ġof Ġfour Ġstructured Ġpercept ron Ġclass ifiers . ĠWe Ġtrain Ġfour Ġclass ifiers Ġto Ġpredict Ġthe Ġpaths Ġof Ġlength Ġ2 , Ġ3 , Ġ4 Ġand Ġ5 . ĠThese Ġset Ġof Ġclass ifiers Ġwere Ġtrained Ġon Ġseparate Ġannot ated Ġdata , Ġand Ġthe Ġfeatures Ġused Ġwere Ġthe Ġsame Ġas Ġin Ġthe Ġset Ġof Ġclass ifiers Ġfor Ġpath Ġlength Ġprediction . </s> Exper iments Ġand ĠResults </s> A Ġparser Ġmodel Ġwas Ġtrained Ġfor ĠHindi Ġusing Ġthe ĠM ST Parser ĠB IB REF 2 Ġby Ġa Ġpart Ġof Ġthe Ġthe ĠHindi ĠDepend ency ĠTree bank Ġdata Ġ( 18 708 Ġsentences ) Ġfrom ĠII IT - Hy der abad ĠB IB REF 3 Ġ. ĠA Ġpart Ġof Ġthe ĠPenn ĠTree bank Ġ( 28 188 Ġsentences ) Ġwas Ġused Ġfor Ġtraining Ġan ĠEnglish Ġparser ĠB IB REF 4 Ġ. ĠThe Ġtree banks Ġwere Ġconverted Ġto ĠM ST Parser Ġformat Ġfrom ĠCon LL Ġformat Ġfor Ġtraining . ĠA Ġpart Ġof Ġthe ĠI LC I ĠEnglish - H ind i ĠTourism Ġparallel Ġcorpus Ġ( 1500 Ġsentences ) Ġwas Ġused Ġfor Ġtraining Ġthe Ġclass ifiers . ĠThis Ġcorpus Ġwas ĠPOS - tag ged Ġusing Ġthe ĠStanford ĠPOS ĠT agger ĠB IB REF 5 Ġfor ĠEnglish Ġand Ġusing Ġthe ĠHindi ĠPOS ĠT agger ĠB IB REF 6 Ġfrom ĠII IT - Hy der abad Ġfor ĠHindi . ĠIt Ġwas Ġthen Ġautomatically Ġannot ated Ġwith Ġdependency Ġparse Ġtrees Ġby Ġthe Ġpars ers Ġwe Ġhad Ġtrained Ġbefore ĠEnglish Ġand ĠHindi . </s> For Ġtesting , Ġwe Ġcreated Ġa Ġcorpus Ġof Ġ100 Ġparallel Ġsentences Ġand Ġtheir Ġword Ġalign ments Ġfrom Ġthe ĠHindi - English ĠTourism Ġparallel Ġcorpus . ĠWe Ġmanually Ġannot ated Ġthe Ġinstances Ġof Ġpp - att achment Ġambiguity . ĠWe Ġexamine Ġthe Ġprediction Ġfor Ġattachment Ġof Ġonly Ġthese Ġcases . ĠThe Ġbaseline Ġsystem Ġused Ġis Ġthe Ġattachment Ġpredicted Ġby Ġthe Ġparser Ġmodels Ġtrained Ġusing Ġthe ĠM ST Parser . ĠWe Ġran Ġexperiments Ġon Ġthe Ġtest Ġset Ġfor Ġiterations Ġ10 Ġto Ġ60 , Ġin Ġsteps Ġof Ġ10 . ĠThe Ġoutputs Ġfrom Ġthe ĠM ST Parser Ġtrained Ġmodel Ġand Ġthe ĠDD Ġalgorithm Ġwere Ġcompared Ġagainst Ġthe Ġgold Ġdata Ġfor ĠEnglish . </s> Our Ġobservations Ġhave Ġbeen Ġtab ulated Ġin ĠTable ĠT AB REF 10 Ġ. ĠThe ĠM ST Parser Ġmodel Ġwas Ġable Ġto Ġcorrectly Ġdis amb ig uate Ġ54 Ġnumber Ġof ĠPP - attach ments . ĠOur Ġalgorithm , Ġhowever , Ġperformed Ġbetter Ġand Ġmarked Ġ64 Ġnumber Ġof Ġattachments Ġcorrectly , Ġin Ġthe Ġbest Ġcase . ĠThe Ġbaseline Ġaccuracy Ġfor ĠPP Ġattachment Ġwas Ġ54 %. ĠWith Ġour Ġapproach , Ġwe Ġwere Ġable Ġto Ġachieve Ġan Ġimprovement Ġof Ġ10 % Ġover Ġthe Ġbaseline . </s> We Ġalso Ġexperimented Ġwith Ġthe Ġnumber Ġof Ġiterations Ġto Ġsee Ġif Ġthe Ġattachment Ġpredictions Ġgot Ġany Ġbetter . ĠThe Ġobservations Ġhave Ġbeen Ġplotted Ġin Ġthe Ġgraph Ġin Ġfigure ĠFIG REF 11 Ġ. ĠOur Ġalgorithm Ġperformed Ġbest Ġat Ġ30 Ġiterations . </s> In Ġthe Ġevent Ġof Ġlack Ġof Ġgold Ġstandard Ġdata Ġfor Ġour Ġexperiments , Ġwe Ġhave Ġused Ġstatistical ĠPOS Ġtag gers Ġfor ĠPOS Ġtagging Ġthe Ġdata . ĠAlso , Ġfor Ġgetting Ġword Ġalign ments , Ġwe Ġhave Ġused ĠG IZ A ++ ĠB IB REF 7 Ġ, Ġwhich Ġagain Ġhas Ġscope Ġfor Ġerrors . ĠThese Ġkind Ġof Ġerrors Ġmay Ġcascade Ġand Ġcause Ġour Ġsystem Ġto Ġunder per form . </s> Conclusion Ġand ĠFuture ĠWork </s> We Ġwere Ġable Ġto Ġachieve Ġan Ġaccuracy Ġof Ġ10 % Ġover Ġthe Ġbaseline Ġusing Ġour Ġapproach . ĠHowever , Ġin Ġterms Ġof Ġoverall Ġdependency Ġparsing Ġand Ġnot Ġjust Ġwith Ġrespect Ġto ĠPP - att achment , Ġour Ġsystem Ġis Ġunable Ġto Ġbeat Ġthe ĠM ST Parser Ġmodel . ĠHowever , Ġwe Ġneed Ġto Ġtest Ġour Ġapproach Ġon Ġa Ġlarger Ġdataset , Ġand Ġacross Ġother Ġdomains Ġbesides ĠTourism . ĠBesides ĠHindi , Ġthere Ġis Ġalso Ġscope Ġfor Ġexploring Ġother Ġlanguages Ġas Ġan Ġaid Ġfor Ġpp - att achment Ġdis amb ig uation Ġin ĠEnglish . ĠOur Ġapproach Ġcould Ġalso Ġbe Ġused Ġfor Ġwh - cl ause Ġattachment . ĠSince Ġincorrect Ġpp - att achment Ġhas Ġa Ġdirect Ġconsequence Ġon ĠMachine ĠTranslation , Ġone Ġinteresting Ġanalysis Ġcould Ġbe Ġto Ġuse Ġpp - attach ments Ġfrom Ġour Ġsystem Ġand Ġcheck Ġfor Ġimprovement Ġin Ġthe Ġquality Ġof Ġtranslation .
Introduction Prepositional Phrase (PP) attachment disambiguation is an important problem in NLP, for it often gives rise to incorrect parse trees . Statistical parsers often predict incorrect attachment for prepositional phrases. For applications like Machine Translation, incorrect PP-attachment leads to serious errors in translation. Several approaches have been proposed to solve this problem. We attempt to tackle this problem for English. English is a syntactically ambiguous language with respect to PP attachments. For example, consider the following sentence where the prepositional phrase with pockets may attach either to the verb washed or to the noun jeans. Sentence INLINEFORM0 : I washed the jeans with pockets. Below is the correct dependency parse tree (for sentence 1) where the prepositional phrase with pockets is attached to the noun jeans. Another possible parse tree for the same sentence could be as shown in Figure FIGREF2 : A statistical parser often predicts the PP-attachment incorrectly, and may lead to incorrect parse trees. Let us now look at another sentence. Sentence INLINEFORM0 : I washed the jeans with soap. The correct dependency tree for sentence INLINEFORM0 is the following (Figure FIGREF3 ), where the prepositional phrase with soap attaches to the verb washed. Clearly, there is a case of ambiguity that can be resolved only if the semantics are known. In this case, the fact that soap is an aid to the verb washed disambiguates its attachment to the verb rather than the noun jeans. For correctly translating such an English sentence to another language, the attachments need to be marked correctly. In this work, we propose a Dual Decomposition (DD) based algorithm for solving the PP attachment problem. We try to disambiguate the PP attachments for English using the corresponding parallel Hindi corpora. Hindi is a syntactically rich language and in most cases exhibits no attachment ambiguities. The use of case markers and the inherent construction of sentences in Hindi make cases of ambiguity rarer. Let us examine how sentences 1 and 2 would look like in Hindi, and if there is a case for ambiguity. Sentence (3) and sentence (4) are the respective Hindi translations of sentence (1) and (2)). Sentence INLINEFORM0 : mn jb vAlF jF06ws DoyF INLINEFORM1 Sentence INLINEFORM0 : mn sAbn s jF06ws DoyF INLINEFORM1 In sentence (3), the prepositional phrase jeb waali attaches to the noun jeans as shown in the figure FIGREF4 . The parse tree for sentence (4) is shown in figure FIGREF5 , where the prepositional phrase saabun se attaches to the verb dhoyee. The case markers waali and se in the two sentences in Hindi make the pp-atttachment clear. In our approach, we make use of the parallel Hindi sentences to disambiguate the PP attachments for English sentences. The roadmap of the paper is as follows: We discuss the literature and related work for solving the PP-attachment problem in section [ SECREF2 ]. Section [ SECREF3 ] describes our approach, and the Dual Decomposition algorithm in detail. The setup, data, and experiments are covered in Section [ SECREF4 ]. With Section [ SECREF5 ], we conclude our work and discuss scope for future work. Related Work A number of supervised and unsupervised approaches for solving the PP-attachment problem have been proposed in the literature. Ratnaparkhi:94 use a Maximum Entropy Model for solving the PP-attachment decision. Schwartz:03 propose an unsupervised approach for solving PP attachment using multilingual aligned data. They transform the data into high-level linguistic representations and use it make reattachment decisions. The intuition is similar to our work, but the approach is entirely different. Brill:94 discuss a transformation-based rule derivation method for PP-attachment disambiguation. It is a simple learning algorithm which derives a set of transformation rules from training corpus, which are then used for solving the PP-attachment problem. Stetina:97 make use of the semantic dictionary to solve the problem of disambiguating PP attachments. Their work describes use of word sense disambiguation (WSD) for both supervised and unsupervised techniques. Agirre Agirre:08 and Medimi Medimi:07 have used WSD-based strategies in different capacities to solve the problem of PP-attachment. Olteanu:05 have attempted to solve the pp-attachment problem as a classification problem of attachment either to the preceding verb or the noun, and have used Support Vector Machines (SVMs) that use complex syntactic and semantic features. Our Approach We propose a Dual Decomposition based inference algorithm to look at the problem of PP-attachment disambiguation. Dual decomposition, or more generally, Lagrangian Relaxation, is a classical method for combinatorial optimization and has been applied to several inference problems in NLP BIBREF0 . We train two separate parser models for English and Hindi each, using the MSTParser, and make use of these models in the inferencing step. The input to the algorithm is a parallel English-Hindi sentence pair, with its word alignments given. We first obtain the predicted parse trees for the English and Hindi sentences from the respective trained parser models as an initialsiation step. The DD algorithm then tries to enforce agreement between the two parse trees subject to the given alignments. Let us take a closer look at what we mean by agreement between the two parse trees. Essentially, if we have two words in the English sentence denoted by i and i', aligned to words j and j' in the parallel Hindi sentence respectively, we can expect a dependency edge between i and i' in the English parse tree to correspond to an edge between j and j' in the Hindi parse tree, and vice versa. Also, in order to accommodate structural diversity in languages BIBREF1 , we can expect an edge in the parse tree in one language to correspond to more than one edge, or rather, a path, in the other language parse tree. This has been captured in the examples in figures FIGREF6 (A) and FIGREF6 (B). For an edge in the English parse tree, we term the corresponding edge or path in the Hindi parse tree as the projection or projected path of the English edge on the Hindi parse tree, and similarly there are projected paths from Hindi to English. For matters of simplicity, we ignore the direction of the edges in the parse trees. The dual decomposition inference algorithm tries to bring the parse trees in the two languages through its constraints. The problem is formulated as below: In the above formulation, INLINEFORM0 and INLINEFORM1 represent a English and Hindi sentence respectively. INLINEFORM2 and INLINEFORM3 are the corresponding parse trees. INLINEFORM4 and INLINEFORM5 are the model parameters for the edge-factored parser models trained for English and Hindi respectively. INLINEFORM6 represents an edge in the English parse tree INLINEFORM7 . INLINEFORM8 is a projected path in Hindi parse tree INLINEFORM9 for a given English edge INLINEFORM10 . The term INLINEFORM11 stands for the score of a projected path in Hindi parse tree INLINEFORM12 for a given English edge INLINEFORM13 . The score of the projected path is calculated as the sum of scores of all edges in the path. Let INLINEFORM0 denote the projected path on sentence h in Hindi for the edge INLINEFORM1 in the English parse tree. We assume INLINEFORM2 where INLINEFORM3 is the score of edge a in the projected path INLINEFORM4 . In the other direction, INLINEFORM5 and INLINEFORM6 is similarly defined. To solve this maximization problem in figure FIGREF7 , we assume one tree to be given and maximize the other and the score of its projected path. The algorithm is described in detail in section SECREF8 . Dual Decomposition based Algorithm We use an iterative Coordinate Descent algorithm (Algorithm 1) which calls the Project Algorithm until convergence. The trees INLINEFORM0 and INLINEFORM1 are initialized by the previously trained parser models for the respective languages. Coordinate Descent Algorithm [1] Initialize INLINEFORM0 and INLINEFORM1 from the MSTParser models for INLINEFORM2 to INLINEFORM3 INLINEFORM4 INLINEFORM5 if INLINEFORM6 or INLINEFORM7 break else INLINEFORM8 INLINEFORM9 end for For INLINEFORM0 iterations, the project function returns a parse tree for English which maximizes the agreement between the English and Hindi parse tree when the Hindi parse tree is fixed, and likewise for the Hindi parse tree. The algorithm converges when the trees no longer change, Let us now look at the Project algorithm (Algorithm 2) in detail. It predicts the tree for a sentence in the target language, given the parse tree in the source language, and the word alignments between the parallel sentence. Project Algorithm (tree T, sen S) [1] A parse tree T (Hindi) and sentence S (English) Initialize INLINEFORM0 INLINEFORM1 = 0 for INLINEFORM2 to INLINEFORM3 INLINEFORM4 for INLINEFORM5 INLINEFORM6 INLINEFORM0 end for if INLINEFORM1 then return INLINEFORM2 else INLINEFORM3 INLINEFORM4 end for The lagrangian multipliers are initialized to zero. The best tree in the target language is predicted by the argmax computation in step 4. This maximization involves the parser model parameters INLINEFORM0 and the score of the best projected path in the source tree for all edges. INLINEFORM1 denotes the score of the projected path of the edge INLINEFORM2 on the source tree T. In steps 6 and 7, the best projected path for every edge of the source tree is predicted on the target tree using the classifiers described in section . The constraints here are that the edges in the projected paths from the classifiers and the predicted trees are in agreement. Projected Path Prediction In order to predict the projected path in one language for an edge in the other language, we use a set of two classifiers in a pipeline. Let us recall that we have two nodes in one language with an edge between them, and we are trying to predict the path of the corresponding aligned nodes in the other language. The first classifier predicts the length of the projected path, and the second predicts the predicted path itself, given the path length from the first classifier. Let us look at these classifiers separately. The classifier for path length prediction is a set of five binary classifiers, which predict the path length to be 1, 2, 3, 4 or 5. We assume projected path lengths to be no greater than 5. These classifiers are perceptrons trained on separate annotated data. The features used were the words and POS tags of the four nodes in the pair of alignments under consideration. The classifier for path prediction is a set of four structured perceptron classifiers. We train four classifiers to predict the paths of length 2, 3, 4 and 5. These set of classifiers were trained on separate annotated data, and the features used were the same as in the set of classifiers for path length prediction. Experiments and Results A parser model was trained for Hindi using the MSTParser BIBREF2 by a part of the the Hindi Dependency Treebank data (18708 sentences) from IIIT-Hyderabad BIBREF3 . A part of the Penn Treebank (28188 sentences) was used for training an English parser BIBREF4 . The treebanks were converted to MSTParser format from ConLL format for training. A part of the ILCI English-Hindi Tourism parallel corpus (1500 sentences) was used for training the classifiers. This corpus was POS-tagged using the Stanford POS Tagger BIBREF5 for English and using the Hindi POS Tagger BIBREF6 from IIIT-Hyderabad for Hindi. It was then automatically annotated with dependency parse trees by the parsers we had trained before English and Hindi. For testing, we created a corpus of 100 parallel sentences and their word alignments from the Hindi-English Tourism parallel corpus. We manually annotated the instances of pp-attachment ambiguity. We examine the prediction for attachment of only these cases. The baseline system used is the attachment predicted by the parser models trained using the MSTParser. We ran experiments on the test set for iterations 10 to 60, in steps of 10. The outputs from the MSTParser trained model and the DD algorithm were compared against the gold data for English. Our observations have been tabulated in Table TABREF10 . The MSTParser model was able to correctly disambiguate 54 number of PP-attachments. Our algorithm, however, performed better and marked 64 number of attachments correctly, in the best case. The baseline accuracy for PP attachment was 54%. With our approach, we were able to achieve an improvement of 10% over the baseline. We also experimented with the number of iterations to see if the attachment predictions got any better. The observations have been plotted in the graph in figure FIGREF11 . Our algorithm performed best at 30 iterations. In the event of lack of gold standard data for our experiments, we have used statistical POS taggers for POS tagging the data. Also, for getting word alignments, we have used GIZA++ BIBREF7 , which again has scope for errors. These kind of errors may cascade and cause our system to underperform. Conclusion and Future Work We were able to achieve an accuracy of 10% over the baseline using our approach. However, in terms of overall dependency parsing and not just with respect to PP-attachment, our system is unable to beat the MSTParser model. However, we need to test our approach on a larger dataset, and across other domains besides Tourism. Besides Hindi, there is also scope for exploring other languages as an aid for pp-attachment disambiguation in English. Our approach could also be used for wh-clause attachment. Since incorrect pp-attachment has a direct consequence on Machine Translation, one interesting analysis could be to use pp-attachments from our system and check for improvement in the quality of translation.
What is the size of the parallel corpus used to train the model constraints?
9cf070d6671ee4a6353f79a165aa648309e01295
What Ġis Ġthe Ġsize Ġof Ġthe Ġparallel Ġcorpus Ġused Ġto Ġtrain Ġthe Ġmodel Ġconstraints ?
1500 Ġsentences
<s> What Ġis Ġthe Ġsize Ġof Ġthe Ġparallel Ġcorpus Ġused Ġto Ġtrain Ġthe Ġmodel Ġconstraints ? </s> Introduction </s> Pre pos itional ĠPh rase Ġ( PP ) Ġattachment Ġdis amb ig uation Ġis Ġan Ġimportant Ġproblem Ġin ĠN LP , Ġfor Ġit Ġoften Ġgives Ġrise Ġto Ġincorrect Ġparse Ġtrees Ġ. ĠStatistical Ġpars ers Ġoften Ġpredict Ġincorrect Ġattachment Ġfor Ġpre pos itional Ġphrases . ĠFor Ġapplications Ġlike ĠMachine ĠTranslation , Ġincorrect ĠPP - att achment Ġleads Ġto Ġserious Ġerrors Ġin Ġtranslation . ĠSeveral Ġapproaches Ġhave Ġbeen Ġproposed Ġto Ġsolve Ġthis Ġproblem . ĠWe Ġattempt Ġto Ġtackle Ġthis Ġproblem Ġfor ĠEnglish . ĠEnglish Ġis Ġa Ġsynt act ically Ġambiguous Ġlanguage Ġwith Ġrespect Ġto ĠPP Ġattachments . ĠFor Ġexample , Ġconsider Ġthe Ġfollowing Ġsentence Ġwhere Ġthe Ġpre pos itional Ġphrase Ġwith Ġpockets Ġmay Ġattach Ġeither Ġto Ġthe Ġverb Ġwashed Ġor Ġto Ġthe Ġnoun Ġjeans . </s> Sent ence ĠIN LINE FORM 0 Ġ: ĠI Ġwashed Ġthe Ġjeans Ġwith Ġpockets . </s> Below Ġis Ġthe Ġcorrect Ġdependency Ġparse Ġtree Ġ( for Ġsentence Ġ1 ) Ġwhere Ġthe Ġpre pos itional Ġphrase Ġwith Ġpockets Ġis Ġattached Ġto Ġthe Ġnoun Ġjeans . </s> Another Ġpossible Ġparse Ġtree Ġfor Ġthe Ġsame Ġsentence Ġcould Ġbe Ġas Ġshown Ġin ĠFigure ĠFIG REF 2 Ġ: </s> A Ġstatistical Ġparser Ġoften Ġpredicts Ġthe ĠPP - att achment Ġincorrectly , Ġand Ġmay Ġlead Ġto Ġincorrect Ġparse Ġtrees . ĠLet Ġus Ġnow Ġlook Ġat Ġanother Ġsentence . </s> Sent ence ĠIN LINE FORM 0 Ġ: ĠI Ġwashed Ġthe Ġjeans Ġwith Ġsoap . </s> The Ġcorrect Ġdependency Ġtree Ġfor Ġsentence ĠIN LINE FORM 0 Ġis Ġthe Ġfollowing Ġ( Figure ĠFIG REF 3 Ġ), Ġwhere Ġthe Ġpre pos itional Ġphrase Ġwith Ġsoap Ġattaches Ġto Ġthe Ġverb Ġwashed . </s> Clearly , Ġthere Ġis Ġa Ġcase Ġof Ġambiguity Ġthat Ġcan Ġbe Ġresolved Ġonly Ġif Ġthe Ġsemantics Ġare Ġknown . ĠIn Ġthis Ġcase , Ġthe Ġfact Ġthat Ġsoap Ġis Ġan Ġaid Ġto Ġthe Ġverb Ġwashed Ġdis amb ig uates Ġits Ġattachment Ġto Ġthe Ġverb Ġrather Ġthan Ġthe Ġnoun Ġjeans . ĠFor Ġcorrectly Ġtranslating Ġsuch Ġan ĠEnglish Ġsentence Ġto Ġanother Ġlanguage , Ġthe Ġattachments Ġneed Ġto Ġbe Ġmarked Ġcorrectly . </s> In Ġthis Ġwork , Ġwe Ġpropose Ġa ĠDual ĠDec om position Ġ( DD ) Ġbased Ġalgorithm Ġfor Ġsolving Ġthe ĠPP Ġattachment Ġproblem . ĠWe Ġtry Ġto Ġdis amb ig uate Ġthe ĠPP Ġattachments Ġfor ĠEnglish Ġusing Ġthe Ġcorresponding Ġparallel ĠHindi Ġcorpor a . ĠHindi Ġis Ġa Ġsynt act ically Ġrich Ġlanguage Ġand Ġin Ġmost Ġcases Ġexhibits Ġno Ġattachment Ġambig u ities . ĠThe Ġuse Ġof Ġcase Ġmarkers Ġand Ġthe Ġinherent Ġconstruction Ġof Ġsentences Ġin ĠHindi Ġmake Ġcases Ġof Ġambiguity Ġrare r . ĠLet Ġus Ġexamine Ġhow Ġsentences Ġ1 Ġand Ġ2 Ġwould Ġlook Ġlike Ġin ĠHindi , Ġand Ġif Ġthere Ġis Ġa Ġcase Ġfor Ġambiguity . </s> Sent ence Ġ( 3 ) Ġand Ġsentence Ġ( 4 ) Ġare Ġthe Ġrespective ĠHindi Ġtranslations Ġof Ġsentence Ġ( 1 ) Ġand Ġ( 2 )). </s> Sent ence ĠIN LINE FORM 0 Ġ: Ġm n Ġj b Ġv Al F Ġj F 06 ws ĠD oy F ĠIN LINE FORM 1 </s> Sent ence ĠIN LINE FORM 0 Ġ: Ġm n Ġs Ab n Ġs Ġj F 06 ws ĠD oy F ĠIN LINE FORM 1 </s> In Ġsentence Ġ( 3 ), Ġthe Ġpre pos itional Ġphrase Ġj eb Ġwa ali Ġattaches Ġto Ġthe Ġnoun Ġjeans Ġas Ġshown Ġin Ġthe Ġfigure ĠFIG REF 4 Ġ. </s> The Ġparse Ġtree Ġfor Ġsentence Ġ( 4 ) Ġis Ġshown Ġin Ġfigure ĠFIG REF 5 Ġ, Ġwhere Ġthe Ġpre pos itional Ġphrase Ġsa ab un Ġse Ġattaches Ġto Ġthe Ġverb Ġdh oy ee . </s> The Ġcase Ġmarkers Ġwa ali Ġand Ġse Ġin Ġthe Ġtwo Ġsentences Ġin ĠHindi Ġmake Ġthe Ġpp - at tt achment Ġclear . ĠIn Ġour Ġapproach , Ġwe Ġmake Ġuse Ġof Ġthe Ġparallel ĠHindi Ġsentences Ġto Ġdis amb ig uate Ġthe ĠPP Ġattachments Ġfor ĠEnglish Ġsentences . ĠThe Ġroadmap Ġof Ġthe Ġpaper Ġis Ġas Ġfollows : ĠWe Ġdiscuss Ġthe Ġliterature Ġand Ġrelated Ġwork Ġfor Ġsolving Ġthe ĠPP - att achment Ġproblem Ġin Ġsection Ġ[ ĠSEC REF 2 Ġ]. ĠSection Ġ[ ĠSEC REF 3 Ġ] Ġdescribes Ġour Ġapproach , Ġand Ġthe ĠDual ĠDec om position Ġalgorithm Ġin Ġdetail . ĠThe Ġsetup , Ġdata , Ġand Ġexperiments Ġare Ġcovered Ġin ĠSection Ġ[ ĠSEC REF 4 Ġ]. ĠWith ĠSection Ġ[ ĠSEC REF 5 Ġ], Ġwe Ġconclude Ġour Ġwork Ġand Ġdiscuss Ġscope Ġfor Ġfuture Ġwork . </s> Related ĠWork </s> A Ġnumber Ġof Ġsupervised Ġand Ġun super vised Ġapproaches Ġfor Ġsolving Ġthe ĠPP - att achment Ġproblem Ġhave Ġbeen Ġproposed Ġin Ġthe Ġliterature . ĠRat n ap ark hi : 94 Ġuse Ġa ĠMaximum ĠEnt ropy ĠModel Ġfor Ġsolving Ġthe ĠPP - att achment Ġdecision . ĠSchwartz : 03 Ġpropose Ġan Ġun super vised Ġapproach Ġfor Ġsolving ĠPP Ġattachment Ġusing Ġmult ilingual Ġaligned Ġdata . ĠThey Ġtransform Ġthe Ġdata Ġinto Ġhigh - level Ġlinguistic Ġrepresentations Ġand Ġuse Ġit Ġmake Ġre att achment Ġdecisions . ĠThe Ġintuition Ġis Ġsimilar Ġto Ġour Ġwork , Ġbut Ġthe Ġapproach Ġis Ġentirely Ġdifferent . ĠBrill : 94 Ġdiscuss Ġa Ġtransformation - based Ġrule Ġderiv ation Ġmethod Ġfor ĠPP - att achment Ġdis amb ig uation . ĠIt Ġis Ġa Ġsimple Ġlearning Ġalgorithm Ġwhich Ġderives Ġa Ġset Ġof Ġtransformation Ġrules Ġfrom Ġtraining Ġcorpus , Ġwhich Ġare Ġthen Ġused Ġfor Ġsolving Ġthe ĠPP - att achment Ġproblem . ĠSt et ina : 97 Ġmake Ġuse Ġof Ġthe Ġsemantic Ġdictionary Ġto Ġsolve Ġthe Ġproblem Ġof Ġdis amb ig uating ĠPP Ġattachments . ĠTheir Ġwork Ġdescribes Ġuse Ġof Ġword Ġsense Ġdis amb ig uation Ġ( W SD ) Ġfor Ġboth Ġsupervised Ġand Ġun super vised Ġtechniques . ĠAg ir re ĠAg ir re : 08 Ġand ĠMed imi ĠMed imi : 07 Ġhave Ġused ĠW SD - based Ġstrategies Ġin Ġdifferent Ġcapacities Ġto Ġsolve Ġthe Ġproblem Ġof ĠPP - att achment . ĠOl te anu : 05 Ġhave Ġattempted Ġto Ġsolve Ġthe Ġpp - att achment Ġproblem Ġas Ġa Ġclassification Ġproblem Ġof Ġattachment Ġeither Ġto Ġthe Ġpreceding Ġverb Ġor Ġthe Ġnoun , Ġand Ġhave Ġused ĠSupport ĠVector ĠMachines Ġ( S V Ms ) Ġthat Ġuse Ġcomplex Ġsynt actic Ġand Ġsemantic Ġfeatures . </s> Our ĠApproach </s> We Ġpropose Ġa ĠDual ĠDec om position Ġbased Ġinference Ġalgorithm Ġto Ġlook Ġat Ġthe Ġproblem Ġof ĠPP - att achment Ġdis amb ig uation . ĠDual Ġdecom position , Ġor Ġmore Ġgenerally , ĠLag rang ian ĠRelax ation , Ġis Ġa Ġclassical Ġmethod Ġfor Ġcomb inator ial Ġoptimization Ġand Ġhas Ġbeen Ġapplied Ġto Ġseveral Ġinference Ġproblems Ġin ĠN LP ĠB IB REF 0 Ġ. ĠWe Ġtrain Ġtwo Ġseparate Ġparser Ġmodels Ġfor ĠEnglish Ġand ĠHindi Ġeach , Ġusing Ġthe ĠM ST Parser , Ġand Ġmake Ġuse Ġof Ġthese Ġmodels Ġin Ġthe Ġinf eren cing Ġstep . ĠThe Ġinput Ġto Ġthe Ġalgorithm Ġis Ġa Ġparallel ĠEnglish - H ind i Ġsentence Ġpair , Ġwith Ġits Ġword Ġalign ments Ġgiven . ĠWe Ġfirst Ġobtain Ġthe Ġpredicted Ġparse Ġtrees Ġfor Ġthe ĠEnglish Ġand ĠHindi Ġsentences Ġfrom Ġthe Ġrespective Ġtrained Ġparser Ġmodels Ġas Ġan Ġinitials iation Ġstep . ĠThe ĠDD Ġalgorithm Ġthen Ġtries Ġto Ġenforce Ġagreement Ġbetween Ġthe Ġtwo Ġparse Ġtrees Ġsubject Ġto Ġthe Ġgiven Ġalign ments . </s> Let Ġus Ġtake Ġa Ġcloser Ġlook Ġat Ġwhat Ġwe Ġmean Ġby Ġagreement Ġbetween Ġthe Ġtwo Ġparse Ġtrees . ĠEssentially , Ġif Ġwe Ġhave Ġtwo Ġwords Ġin Ġthe ĠEnglish Ġsentence Ġden oted Ġby Ġi Ġand Ġi ', Ġaligned Ġto Ġwords Ġj Ġand Ġj ' Ġin Ġthe Ġparallel ĠHindi Ġsentence Ġrespectively , Ġwe Ġcan Ġexpect Ġa Ġdependency Ġedge Ġbetween Ġi Ġand Ġi ' Ġin Ġthe ĠEnglish Ġparse Ġtree Ġto Ġcorrespond Ġto Ġan Ġedge Ġbetween Ġj Ġand Ġj ' Ġin Ġthe ĠHindi Ġparse Ġtree , Ġand Ġvice Ġversa . ĠAlso , Ġin Ġorder Ġto Ġaccommodate Ġstructural Ġdiversity Ġin Ġlanguages ĠB IB REF 1 Ġ, Ġwe Ġcan Ġexpect Ġan Ġedge Ġin Ġthe Ġparse Ġtree Ġin Ġone Ġlanguage Ġto Ġcorrespond Ġto Ġmore Ġthan Ġone Ġedge , Ġor Ġrather , Ġa Ġpath , Ġin Ġthe Ġother Ġlanguage Ġparse Ġtree . ĠThis Ġhas Ġbeen Ġcaptured Ġin Ġthe Ġexamples Ġin Ġfigures ĠFIG REF 6 Ġ( A ) Ġand ĠFIG REF 6 Ġ( B ). ĠFor Ġan Ġedge Ġin Ġthe ĠEnglish Ġparse Ġtree , Ġwe Ġterm Ġthe Ġcorresponding Ġedge Ġor Ġpath Ġin Ġthe ĠHindi Ġparse Ġtree Ġas Ġthe Ġprojection Ġor Ġprojected Ġpath Ġof Ġthe ĠEnglish Ġedge Ġon Ġthe ĠHindi Ġparse Ġtree , Ġand Ġsimilarly Ġthere Ġare Ġprojected Ġpaths Ġfrom ĠHindi Ġto ĠEnglish . ĠFor Ġmatters Ġof Ġsimplicity , Ġwe Ġignore Ġthe Ġdirection Ġof Ġthe Ġedges Ġin Ġthe Ġparse Ġtrees . ĠThe Ġdual Ġdecom position Ġinference Ġalgorithm Ġtries Ġto Ġbring Ġthe Ġparse Ġtrees Ġin Ġthe Ġtwo Ġlanguages Ġthrough Ġits Ġconstraints . </s> The Ġproblem Ġis Ġformulated Ġas Ġbelow : </s> In Ġthe Ġabove Ġformulation , ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġrepresent Ġa ĠEnglish Ġand ĠHindi Ġsentence Ġrespectively . ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġare Ġthe Ġcorresponding Ġparse Ġtrees . ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġare Ġthe Ġmodel Ġparameters Ġfor Ġthe Ġedge - fact ored Ġparser Ġmodels Ġtrained Ġfor ĠEnglish Ġand ĠHindi Ġrespectively . ĠIN LINE FORM 6 Ġrepresents Ġan Ġedge Ġin Ġthe ĠEnglish Ġparse Ġtree ĠIN LINE FORM 7 Ġ. ĠIN LINE FORM 8 Ġis Ġa Ġprojected Ġpath Ġin ĠHindi Ġparse Ġtree ĠIN LINE FORM 9 Ġfor Ġa Ġgiven ĠEnglish Ġedge ĠIN LINE FORM 10 Ġ. ĠThe Ġterm ĠIN LINE FORM 11 Ġstands Ġfor Ġthe Ġscore Ġof Ġa Ġprojected Ġpath Ġin ĠHindi Ġparse Ġtree ĠIN LINE FORM 12 Ġfor Ġa Ġgiven ĠEnglish Ġedge ĠIN LINE FORM 13 Ġ. </s> The Ġscore Ġof Ġthe Ġprojected Ġpath Ġis Ġcalculated Ġas Ġthe Ġsum Ġof Ġscores Ġof Ġall Ġedges Ġin Ġthe Ġpath . ĠLet ĠIN LINE FORM 0 Ġdenote Ġthe Ġprojected Ġpath Ġon Ġsentence Ġh Ġin ĠHindi Ġfor Ġthe Ġedge ĠIN LINE FORM 1 Ġin Ġthe ĠEnglish Ġparse Ġtree . ĠWe Ġassume ĠIN LINE FORM 2 Ġwhere ĠIN LINE FORM 3 Ġis Ġthe Ġscore Ġof Ġedge Ġa Ġin Ġthe Ġprojected Ġpath ĠIN LINE FORM 4 Ġ. ĠIn Ġthe Ġother Ġdirection , ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġis Ġsimilarly Ġdefined . </s> To Ġsolve Ġthis Ġmaxim ization Ġproblem Ġin Ġfigure ĠFIG REF 7 Ġ, Ġwe Ġassume Ġone Ġtree Ġto Ġbe Ġgiven Ġand Ġmaximize Ġthe Ġother Ġand Ġthe Ġscore Ġof Ġits Ġprojected Ġpath . ĠThe Ġalgorithm Ġis Ġdescribed Ġin Ġdetail Ġin Ġsection ĠSEC REF 8 Ġ. </s> Dual ĠDec om position Ġbased ĠAl gorithm </s> We Ġuse Ġan Ġiter ative ĠCoord inate ĠDes cent Ġalgorithm Ġ( Al gorithm Ġ1 ) Ġwhich Ġcalls Ġthe ĠProject ĠAl gorithm Ġuntil Ġconvergence . ĠThe Ġtrees ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare Ġinitialized Ġby Ġthe Ġpreviously Ġtrained Ġparser Ġmodels Ġfor Ġthe Ġrespective Ġlanguages . </s> Co ordinate ĠDes cent ĠAl gorithm Ġ[ 1 ] ĠInitial ize ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġfrom Ġthe ĠM ST Parser Ġmodels Ġfor ĠIN LINE FORM 2 Ġto ĠIN LINE FORM 3 ĠIN LINE FORM 4 ĠIN LINE FORM 5 Ġif ĠIN LINE FORM 6 Ġor ĠIN LINE FORM 7 Ġbreak Ġelse ĠIN LINE FORM 8 ĠIN LINE FORM 9 Ġend Ġfor </s> For ĠIN LINE FORM 0 Ġiterations , Ġthe Ġproject Ġfunction Ġreturns Ġa Ġparse Ġtree Ġfor ĠEnglish Ġwhich Ġmaxim izes Ġthe Ġagreement Ġbetween Ġthe ĠEnglish Ġand ĠHindi Ġparse Ġtree Ġwhen Ġthe ĠHindi Ġparse Ġtree Ġis Ġfixed , Ġand Ġlikewise Ġfor Ġthe ĠHindi Ġparse Ġtree . ĠThe Ġalgorithm Ġconver ges Ġwhen Ġthe Ġtrees Ġno Ġlonger Ġchange , </s> Let Ġus Ġnow Ġlook Ġat Ġthe ĠProject Ġalgorithm Ġ( Al gorithm Ġ2 ) Ġin Ġdetail . ĠIt Ġpredicts Ġthe Ġtree Ġfor Ġa Ġsentence Ġin Ġthe Ġtarget Ġlanguage , Ġgiven Ġthe Ġparse Ġtree Ġin Ġthe Ġsource Ġlanguage , Ġand Ġthe Ġword Ġalign ments Ġbetween Ġthe Ġparallel Ġsentence . </s> Project ĠAl gorithm Ġ( tree ĠT , Ġsen ĠS ) Ġ[ 1 ] ĠA Ġparse Ġtree ĠT Ġ( H ind i ) Ġand Ġsentence ĠS Ġ( English ) ĠInitial ize ĠIN LINE FORM 0 ĠIN LINE FORM 1 Ġ= Ġ0 Ġfor ĠIN LINE FORM 2 Ġto ĠIN LINE FORM 3 ĠIN LINE FORM 4 Ġfor ĠIN LINE FORM 5 ĠIN LINE FORM 6 </s> IN LINE FORM 0 Ġend Ġfor Ġif ĠIN LINE FORM 1 Ġthen Ġreturn ĠIN LINE FORM 2 Ġelse ĠIN LINE FORM 3 ĠIN LINE FORM 4 Ġend Ġfor </s> The Ġlag rang ian Ġmultipl iers Ġare Ġinitialized Ġto Ġzero . ĠThe Ġbest Ġtree Ġin Ġthe Ġtarget Ġlanguage Ġis Ġpredicted Ġby Ġthe Ġarg max Ġcomputation Ġin Ġstep Ġ4 . ĠThis Ġmaxim ization Ġinvolves Ġthe Ġparser Ġmodel Ġparameters ĠIN LINE FORM 0 Ġand Ġthe Ġscore Ġof Ġthe Ġbest Ġprojected Ġpath Ġin Ġthe Ġsource Ġtree Ġfor Ġall Ġedges . ĠIN LINE FORM 1 Ġdenotes Ġthe Ġscore Ġof Ġthe Ġprojected Ġpath Ġof Ġthe Ġedge ĠIN LINE FORM 2 Ġon Ġthe Ġsource Ġtree ĠT . ĠIn Ġsteps Ġ6 Ġand Ġ7 , Ġthe Ġbest Ġprojected Ġpath Ġfor Ġevery Ġedge Ġof Ġthe Ġsource Ġtree Ġis Ġpredicted Ġon Ġthe Ġtarget Ġtree Ġusing Ġthe Ġclass ifiers Ġdescribed Ġin Ġsection Ġ. ĠThe Ġconstraints Ġhere Ġare Ġthat Ġthe Ġedges Ġin Ġthe Ġprojected Ġpaths Ġfrom Ġthe Ġclass ifiers Ġand Ġthe Ġpredicted Ġtrees Ġare Ġin Ġagreement . </s> Project ed ĠPath ĠPrediction </s> In Ġorder Ġto Ġpredict Ġthe Ġprojected Ġpath Ġin Ġone Ġlanguage Ġfor Ġan Ġedge Ġin Ġthe Ġother Ġlanguage , Ġwe Ġuse Ġa Ġset Ġof Ġtwo Ġclass ifiers Ġin Ġa Ġpipeline . ĠLet Ġus Ġrecall Ġthat Ġwe Ġhave Ġtwo Ġnodes Ġin Ġone Ġlanguage Ġwith Ġan Ġedge Ġbetween Ġthem , Ġand Ġwe Ġare Ġtrying Ġto Ġpredict Ġthe Ġpath Ġof Ġthe Ġcorresponding Ġaligned Ġnodes Ġin Ġthe Ġother Ġlanguage . ĠThe Ġfirst Ġclass ifier Ġpredicts Ġthe Ġlength Ġof Ġthe Ġprojected Ġpath , Ġand Ġthe Ġsecond Ġpredicts Ġthe Ġpredicted Ġpath Ġitself , Ġgiven Ġthe Ġpath Ġlength Ġfrom Ġthe Ġfirst Ġclass ifier . ĠLet Ġus Ġlook Ġat Ġthese Ġclass ifiers Ġseparately . </s> The Ġclass ifier Ġfor Ġpath Ġlength Ġprediction Ġis Ġa Ġset Ġof Ġfive Ġbinary Ġclass ifiers , Ġwhich Ġpredict Ġthe Ġpath Ġlength Ġto Ġbe Ġ1 , Ġ2 , Ġ3 , Ġ4 Ġor Ġ5 . ĠWe Ġassume Ġprojected Ġpath Ġlengths Ġto Ġbe Ġno Ġgreater Ġthan Ġ5 . ĠThese Ġclass ifiers Ġare Ġpercept rons Ġtrained Ġon Ġseparate Ġannot ated Ġdata . ĠThe Ġfeatures Ġused Ġwere Ġthe Ġwords Ġand ĠPOS Ġtags Ġof Ġthe Ġfour Ġnodes Ġin Ġthe Ġpair Ġof Ġalign ments Ġunder Ġconsideration . </s> The Ġclass ifier Ġfor Ġpath Ġprediction Ġis Ġa Ġset Ġof Ġfour Ġstructured Ġpercept ron Ġclass ifiers . ĠWe Ġtrain Ġfour Ġclass ifiers Ġto Ġpredict Ġthe Ġpaths Ġof Ġlength Ġ2 , Ġ3 , Ġ4 Ġand Ġ5 . ĠThese Ġset Ġof Ġclass ifiers Ġwere Ġtrained Ġon Ġseparate Ġannot ated Ġdata , Ġand Ġthe Ġfeatures Ġused Ġwere Ġthe Ġsame Ġas Ġin Ġthe Ġset Ġof Ġclass ifiers Ġfor Ġpath Ġlength Ġprediction . </s> Exper iments Ġand ĠResults </s> A Ġparser Ġmodel Ġwas Ġtrained Ġfor ĠHindi Ġusing Ġthe ĠM ST Parser ĠB IB REF 2 Ġby Ġa Ġpart Ġof Ġthe Ġthe ĠHindi ĠDepend ency ĠTree bank Ġdata Ġ( 18 708 Ġsentences ) Ġfrom ĠII IT - Hy der abad ĠB IB REF 3 Ġ. ĠA Ġpart Ġof Ġthe ĠPenn ĠTree bank Ġ( 28 188 Ġsentences ) Ġwas Ġused Ġfor Ġtraining Ġan ĠEnglish Ġparser ĠB IB REF 4 Ġ. ĠThe Ġtree banks Ġwere Ġconverted Ġto ĠM ST Parser Ġformat Ġfrom ĠCon LL Ġformat Ġfor Ġtraining . ĠA Ġpart Ġof Ġthe ĠI LC I ĠEnglish - H ind i ĠTourism Ġparallel Ġcorpus Ġ( 1500 Ġsentences ) Ġwas Ġused Ġfor Ġtraining Ġthe Ġclass ifiers . ĠThis Ġcorpus Ġwas ĠPOS - tag ged Ġusing Ġthe ĠStanford ĠPOS ĠT agger ĠB IB REF 5 Ġfor ĠEnglish Ġand Ġusing Ġthe ĠHindi ĠPOS ĠT agger ĠB IB REF 6 Ġfrom ĠII IT - Hy der abad Ġfor ĠHindi . ĠIt Ġwas Ġthen Ġautomatically Ġannot ated Ġwith Ġdependency Ġparse Ġtrees Ġby Ġthe Ġpars ers Ġwe Ġhad Ġtrained Ġbefore ĠEnglish Ġand ĠHindi . </s> For Ġtesting , Ġwe Ġcreated Ġa Ġcorpus Ġof Ġ100 Ġparallel Ġsentences Ġand Ġtheir Ġword Ġalign ments Ġfrom Ġthe ĠHindi - English ĠTourism Ġparallel Ġcorpus . ĠWe Ġmanually Ġannot ated Ġthe Ġinstances Ġof Ġpp - att achment Ġambiguity . ĠWe Ġexamine Ġthe Ġprediction Ġfor Ġattachment Ġof Ġonly Ġthese Ġcases . ĠThe Ġbaseline Ġsystem Ġused Ġis Ġthe Ġattachment Ġpredicted Ġby Ġthe Ġparser Ġmodels Ġtrained Ġusing Ġthe ĠM ST Parser . ĠWe Ġran Ġexperiments Ġon Ġthe Ġtest Ġset Ġfor Ġiterations Ġ10 Ġto Ġ60 , Ġin Ġsteps Ġof Ġ10 . ĠThe Ġoutputs Ġfrom Ġthe ĠM ST Parser Ġtrained Ġmodel Ġand Ġthe ĠDD Ġalgorithm Ġwere Ġcompared Ġagainst Ġthe Ġgold Ġdata Ġfor ĠEnglish . </s> Our Ġobservations Ġhave Ġbeen Ġtab ulated Ġin ĠTable ĠT AB REF 10 Ġ. ĠThe ĠM ST Parser Ġmodel Ġwas Ġable Ġto Ġcorrectly Ġdis amb ig uate Ġ54 Ġnumber Ġof ĠPP - attach ments . ĠOur Ġalgorithm , Ġhowever , Ġperformed Ġbetter Ġand Ġmarked Ġ64 Ġnumber Ġof Ġattachments Ġcorrectly , Ġin Ġthe Ġbest Ġcase . ĠThe Ġbaseline Ġaccuracy Ġfor ĠPP Ġattachment Ġwas Ġ54 %. ĠWith Ġour Ġapproach , Ġwe Ġwere Ġable Ġto Ġachieve Ġan Ġimprovement Ġof Ġ10 % Ġover Ġthe Ġbaseline . </s> We Ġalso Ġexperimented Ġwith Ġthe Ġnumber Ġof Ġiterations Ġto Ġsee Ġif Ġthe Ġattachment Ġpredictions Ġgot Ġany Ġbetter . ĠThe Ġobservations Ġhave Ġbeen Ġplotted Ġin Ġthe Ġgraph Ġin Ġfigure ĠFIG REF 11 Ġ. ĠOur Ġalgorithm Ġperformed Ġbest Ġat Ġ30 Ġiterations . </s> In Ġthe Ġevent Ġof Ġlack Ġof Ġgold Ġstandard Ġdata Ġfor Ġour Ġexperiments , Ġwe Ġhave Ġused Ġstatistical ĠPOS Ġtag gers Ġfor ĠPOS Ġtagging Ġthe Ġdata . ĠAlso , Ġfor Ġgetting Ġword Ġalign ments , Ġwe Ġhave Ġused ĠG IZ A ++ ĠB IB REF 7 Ġ, Ġwhich Ġagain Ġhas Ġscope Ġfor Ġerrors . ĠThese Ġkind Ġof Ġerrors Ġmay Ġcascade Ġand Ġcause Ġour Ġsystem Ġto Ġunder per form . </s> Conclusion Ġand ĠFuture ĠWork </s> We Ġwere Ġable Ġto Ġachieve Ġan Ġaccuracy Ġof Ġ10 % Ġover Ġthe Ġbaseline Ġusing Ġour Ġapproach . ĠHowever , Ġin Ġterms Ġof Ġoverall Ġdependency Ġparsing Ġand Ġnot Ġjust Ġwith Ġrespect Ġto ĠPP - att achment , Ġour Ġsystem Ġis Ġunable Ġto Ġbeat Ġthe ĠM ST Parser Ġmodel . ĠHowever , Ġwe Ġneed Ġto Ġtest Ġour Ġapproach Ġon Ġa Ġlarger Ġdataset , Ġand Ġacross Ġother Ġdomains Ġbesides ĠTourism . ĠBesides ĠHindi , Ġthere Ġis Ġalso Ġscope Ġfor Ġexploring Ġother Ġlanguages Ġas Ġan Ġaid Ġfor Ġpp - att achment Ġdis amb ig uation Ġin ĠEnglish . ĠOur Ġapproach Ġcould Ġalso Ġbe Ġused Ġfor Ġwh - cl ause Ġattachment . ĠSince Ġincorrect Ġpp - att achment Ġhas Ġa Ġdirect Ġconsequence Ġon ĠMachine ĠTranslation , Ġone Ġinteresting Ġanalysis Ġcould Ġbe Ġto Ġuse Ġpp - attach ments Ġfrom Ġour Ġsystem Ġand Ġcheck Ġfor Ġimprovement Ġin Ġthe Ġquality Ġof Ġtranslation .