Transacciones de la Asociación de Lingüística Computacional, volumen. 4, páginas. 113–125, 2016. Editor de acciones: Noah Smith.
Lote de envío: 10/2015; Lote de revisión: 2/2016; Publicado 4/2016.
2016 Asociación de Lingüística Computacional. Distribuido bajo CC-BY 4.0 licencia.
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AJointModelforAnswerSentenceRankingandAnswerExtractionMdArafatSultan†VittorioCastelli‡RaduFlorian‡†InstituteofCognitiveScienceandDepartmentofComputerScience,UniversityofColorado,Roca,CO‡IBMT.J.WatsonResearchCenter,YorktownHeights,NYarafat.sultan@colorado.edu,vittorio@us.ibm.com,raduf@us.ibm.comAbstractAnswersentencerankingandanswerextrac-tionaretwokeychallengesinquestionanswer-ingthathavetraditionallybeentreatediniso-lation,i.e.,asindependenttasks.Inthisarti-cle,nosotros(1)explainhowbothtasksarerelatedattheircorebyacommonquantity,y(2)proposeasimpleandintuitivejointprobabilis-ticmodelthataddressesbothviajointcom-putationbuttask-specificapplicationofthatquantity.InourexperimentswithtwoTRECdatasets,ourjointmodelsubstantiallyoutper-formsstate-of-the-artsystemsinbothtasks.1IntroductionOneoftheoriginalgoalsofAIwastobuildmachinesthatcannaturallyinteractwithhumans.Overtime,thechallengesbecameapparentandlanguagepro-cessingemergedasoneofAI’smostpuzzlingareas.Nevertheless,majorbreakthroughshavestillbeenmadeinseveralimportanttasks;withIBM’sWat-son(Ferruccietal.,2010)significantlyoutperform-inghumanchampionsinthequizcontestJeopardy!,questionanswering(control de calidad)isdefinitelyonesuchtask.QAcomesinvariousforms,eachsupportingspe-cifickindsofuserrequirements.Considerascenariowhereasystemisgivenaquestionandasetofsen-tenceseachofwhichmayormaynotcontainananswertothatquestion.Thegoalofanswerextrac-tionistoextractapreciseanswerintheformofashortspanoftextinoneormoreofthosesentences.Inthisform,QAmeetsusers’immediateinformationneeds.Answersentenceranking,por otro lado,isthetaskofassigningaranktoeachsentencesothattheonesthataremorelikelytocontainananswerarerankedhigher.Inthisform,QAissimilartoinforma-tionretrievalandpresentsgreateropportunitiesforfurtherexplorationandlearning.Inthisarticle,weproposeanovelapproachtojointlysolvingthesetwowell-studiedyetopenQAproblems.Mostanswersentencerankingalgorithmsoperateundertheassumptionthatthedegreeofsyntacticand/orsemanticsimilaritybetweenquestionsandan-swersentencesisasufficientlystrongpredictorofanswersentencerelevance(Wangetal.,2007;Yihetal.,2013;Yuetal.,2014;SeverynandMoschitti,2015).Por otro lado,answerextractionalgo-rithmsfrequentlyassesscandidateanswerphrasesbasedprimarilyontheirownpropertiesrelativetothequestion(e.g.,whetherthequestionisawhoquestionandthephrasereferstoaperson),makinginadequateornouseofsentence-levelevidence(Yaoetal.,2013a;SeverynandMoschitti,2013).Boththeseassumptions,sin embargo,aresimplistic,andfailtocapturethecorerequirementsofthetwotasks.Table1showsaquestion,andthreecandi-dateanswersentencesonlyoneofwhich(S(1))ac-tuallyanswersthequestion.Rankingmodelsthatrelysolelyontextsimilarityarehighlylikelytoin-correctlyassignsimilarrankstoS(1)andS(2).SuchmodelswouldfailtoutilizethekeypieceofevidenceagainstS(2)thatitdoesnotcontainanytemporalinformation,necessarytoanswerawhenquestion.Similarly,anextractionmodelthatreliesonlyonthefeaturesofacandidatephrasemightextractthetem-poralexpression“theyear1666”inS(3)asananswerdespiteaclearlackofsentence-levelevidence.Inviewoftheabove,weproposeajointmodel
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QWhenwastheHaleBoppcometdiscov-ered?S(1)ThecometwasfirstspottedbyHaleandBopp,bothUSastronomers,onJuly22,1995.S(2)Hale-Bopp,alargecomet,wasobservedforthefirsttimeinChina.S(3)Thelawofgravitywasdiscoveredintheyear1666bySirIsaacNewton.Table1:Aquestionandthreecandidateanswersentences.foranswersentencerankingandanswerextractionthatutilizesbothsentenceandphrase-levelevidencetosolveeachtask.Moreconcretely,nosotros(1)designtask-specificprobabilisticmodelsforrankingandextraction,exploitingfeaturesofcandidateanswersentencesandtheirphrases,respectivamente,y(2)com-binethetwomodelsinasimple,intuitivesteptobuildajointprobabilisticmodelforbothtasks.Thistwo-stepapproachfacilitatesconstructionofnewjointmodelsfromanyexistingsolutionstothetwotasks.OnapubliclyavailableTRECdataset(Wangetal.,2007),ourjointmodeldemonstratesanimprovementinrankingbyover10MAPandMRRscoresoverthecurrentstateoftheart.Italsooutperformsstate-of-the-artextractionsystemsontwoTRECdatasets(Wangetal.,2007;Yaoetal.,2013c).2BackgroundInthissection,weprovideaformaldescriptionofthetwotasksandestablishterminologythatwefollowinlatersections.TheWangetal.(2007)datasethasbeenthebenchmarkformostrecentworkonthetwotasksaswellasourown.Therefore,wesituateourdescriptioninthespecificcontextofthisdataset.Wealsodiscussrelatedpriorwork.2.1AnswerSentenceRankingGivenaquestionQandasetofcandidatean-swersentences{S(1),…,S(norte)},thegoalinan-swersentencerankingistoassigneachS(i)anintegerrankQ(S(i))sothatforanypair(i,j),rankQ(S(i))
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andextractionarethendrivenbytask-appropriateapplicationofthiscommonquantity.GivenEquation(3),theconditionforrankingisredefinedasfollows:rankQ(S(i))
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otherwise.ItalsocomputesthedegreeofsimilaritysimCbetweenthetwowords’contextsintheirrespectivesentences.Thissimilarityiscomputedasthesumofwordsimilaritiesintwodifferenttypesofcontexts:(1)adependencyneighborhoodofsize2(i.e.parents,grandparents,childrenandgrandchildren),y(2)asurface-formneighborhoodofsize3(i.e.3wordstotheleftand3wordstotheright).Stopwordsareskippedduringneighborselection.UnliketheSultanetal.(2014)aligner,whichallowsasingleneighborwordtobematchedtomultiplesimilarwordsintheothersentence,wematchneighborsusingamax-weightedbipartitematchingalgorithm,wherewordsimilaritiesserveasedgeweights.EverywordpairacrossQandS(i)receivesafinalweightgivenbyw∗simW+(1−w)∗simC,wherew∈[0,1].WhileSultanetal.useagreedybest-firstalgorithmtoalignwordsbasedontheseweights,weusethemasedgeweightsinamax-weightedbipartitematchingofwordpairs(detailsfollow).Weadoptthestrategyoftheoriginalalignerofstartingwithhigh-precisionalignmentsandincreas-ingtherecallinlatersteps.Tothisend,wealigninthefollowingorder:(1)identicalwordsequenceswithatleastonecontentword,(2)namedentities,(3)contentwords,y(4)stopwords.Followingtheoriginalaligner,noadditionalcontextmatchingisperformedinstep1sinceasequenceitselfpro-videscontextualevidenceforitstokens.Foreachofsteps2–4,words/entitiesofthecorrespondingtypearealignedusingmax-weightedbipartitematchingasdescribedabove(multiwordnamedentitiesareconsideredsingleunitsinstep2);otherwordtypesandalreadyalignedwordsarediscarded.ThevaluesofwandppdbSimarederivedusingagridsearchonanalignmentdataset(Brockett,2007).GivenalignedwordsintheQApair,ourfirstfeaturecomputestheproportionofalignedcontentwordsinQandS(i),combined:simA(q,S(i))=nac(q)+nac(S(i))nc(q)+nc(S(i))wherenac(·)andnc(·)representthenumberofalignedcontentwordsandthetotalnumberofcontentwordsinasentence,respectively.S(i)canbearbitrarilylongandstillcontainanan-swertoQ.Intheabovesimilaritymeasure,longeranswersentencesarepenalizedduetoalargernum-berofunalignedwords.Tocounterthisphenomenon,weaddameasureofcoverageofQbyS(i)totheoriginalfeaturesetofSultanetal.(2015):covA(q,S(i))=nac(q)nc(q)4.2ASemanticVectorFeatureNeuralwordembeddings(Mikolovetal.,2013;Ba-ronietal.,2014;Pennington,2014)havebeenhighlysuccessfulasdistributionalwordrepresentationsintherecentpast.Weutilizethe400-dimensionalwordembeddingsdevelopedbyBaronietal.(2014)3toconstructsentence-levelembeddingsforQandS(i),whichwethencomparetocomputeasimilarityscore.ToconstructthevectorrepresentationVSofagivensentenceS,wefirstextractthecontentwordlemmasCS={C(1)S,…,C(METRO)S}inS.Thevectorsrepresentingtheselemmasarethenaddedtogeneratethesentencevector:VS=MXi=1VC(i)SFinally,asimilaritymeasureforQandS(i)isderivedbytakingthecosinesimilaritybetweentheirvectorrepresentations:simE(q,S(i))=VQ·VS(i)|VQ||VS(i)|Thissimplebag-of-wordsmodelwasfoundtoaug-mentthealignment-basedfeaturewellintheevalua-tionsreportedbySultanetal.(2015).simA,covAandsimEconstituteourfinalfeatureset.AsweshowinSection6,thissmallfeaturesetoutperformsthecurrentstateoftheartinanswersentenceranking.5AnswerExtractionFeaturesAsmentionedinSection3.2,weconsideronlyNPchunksasanswercandidatesforextraction.Ourchunkfeaturescanbecategorizedintotwobroadgroups,whichwedescribeinthissection.Forthefollowingdiscussion,dejar(q,S(i),C)beourquestion,answersentence,answerchunktriple.3http://clic.cimec.unitn.it/composes/semantic-vectors.html
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5.1Question-IndependentFeaturesThesefeaturesrepresentpropertiesofcindependentofthenatureofQ.Forexample,ourfirsttwofeaturesfireifallcontentwordsincarepresentinQoraligntowordsinQ.Suchchunksrarelycontainananswer,regardlessofthetypeofQ.Yaoetal.(2013a)reportanobservationthatan-swerchunksoftenappearclosetoalignedcontentwordsofspecifictypesinS(i).Tomodelthisphe-nomenon,weadopttheirfeaturesspecifyingthedis-tanceofcfromthenearestalignedcontentwordwainS(i)andthePOS/DEP/NERtagsofwa.Inaddi-tion,toencodethetotalamountoflocalevidencepresentforc,weemploytheproportionsofalignedcontentwordsinitsdependency(size=2)andsur-face(size=3)contextsinS(i).5.2FeaturesContainingtheQuestionTypeThesefeaturesareoftheform“question-type|x”,wherexcanbeanelementary(i.e.unit)orcompositefeature.Therationaleisthatcertainfeaturesarein-formativeprimarilyinthecontextofcertainquestiontypes(e.g.,alikelyanswertoawhenquestionisachunkcontainingtheNERtagDATE).HeadwordFeatures.WeextracttheheadwordofcanduseitsPOS/DEP/NERtagsasfeatures(ap-pendedtothequestiontype).AheadwordinthesubjectpositionofS(i)orwithPERSONasitsNERtag,forexample,isalikelyanswertoawhoquestion.QuestionFocus.Thequestionfocuswordrepre-sentstheentityaboutwhichthequestionisbeingasked.Forexample,in“Whatisthelargestcoun-tryintheworld?",thefocuswordis“country”.Forquestiontypeslikewhatandwhich,propertiesofthequestionfocuslargelydeterminethenatureofthean-swer.Intheaboveexample,thefocuswordindicatesthatGPEisalikelyNERtagfortheanswer.Weextractthequestionfocususingarule-basedsystemoriginallydesignedforadifferentapplica-tion,undertheassumptionthataquestioncouldspanmultiplesentences.Therule-basedsystemislooselyinspiredbytheworkofLallyetal.(2012),fromwhichitdiffersradicallybecausethequestionsintheJeopardy!gameareexpressedasanswers.Thefocusextractorfirstdeterminesthequestionwordorwords,whichisthenusedinconjunctionwiththeparsetreetodecidewhetherthequestionworditselforsomeotherwordinthesentenceistheactualfocus.WepairtheheadwordPOS/DEP/NERtagswiththefocuswordanditsPOS/NERtags,andaddeachsuchpair(appendedtothequestiontype)toourfeatureset.Thereareninefeatureshere;examplesincludequestion-type|question-focus-word|headword-pos-tagandquestion-type|question-focus-ner-tag|headword-ner-tag.Wealsoemploythetrue/falselabelsofthefollow-ingpropositionsasfeatures(inconjunctionwiththequestiontype):(1)thequestionfocuswordisinc,(2)thequestionfocusPOStagisinthePOStagsofc,y(3)thequestionfocusNERtagisoftheformxorxDESC,andxisintheNERtagsofc,forsomex(e.g.,GPE).ChunkTags.Inmanycases,itisnotthehead-wordofcwhichistheanswer;forexample,inQ:“HowmanystatesarethereintheUS?”andc:“50states”,theheadwordofcis“states”.Toextendourunitofattentionfromtheheadwordtotheen-tirechunk,wefirstconstructvocabulariesofPOSandNERtags,VposandVner,fromtrainingdata.ForeachpossibletaginVpos,wethenusethepres-ence/absenceofthattaginthePOStagsequenceforcasafeature(inconjunctionwiththequestiontype).WerepeattheprocessforVner.Fortheabovec,por ejemplo,aninformativefeaturewhichislikelytofireis:“question-type=how-many|theNERtagsofcincludeCARDINAL”.PartialAlignment.Forsomequestiontypes,partofacorrectanswerchunkisoftenalignedtoaques-tionword(e.g.,Q:“Howmanyplayersareonthefieldduringasoccergame?",C:“22players”).Toin-formourmodelofsuchoccurrences,weemploytwofeatures—true/falselabelsofthefollowingproposi-tions:(1)cispartiallyaligned,(2)cisnotalignedatall(eachinconjunctionwiththequestiontype).6Experiments6.1DataTheWangetal.(2007)corpusiscreatedfromTextREtrievalConference(TREC)8–13QAdata.Itcon-sistsofasetoffactoidquestions,andforeachques-tion,asetofcandidateanswersentences.Eachan-swercandidateisautomaticallydrawnfromalargerdocumentbasedontwoselectioncriteria:(1)anon-zerocontentwordoverlapwiththequestion,o(2)
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Dataset#Questions#QAPairs%PositiveTRAIN-ALL1,22953,41712.0TRAIN944,7187.4DEV821,14819.3TEST1001,51718.7Table2:SummaryoftheWangetal.(2007)corpus.amatchwiththegoldregexpanswerpatternforthequestion(trainingonly).TRAINpairsaredrawnfromTREC8–12;DEVandTESTpairsaredrawnfromTREC13.DetailsoftheTRAIN/DEV/TESTsplitaregiveninTable2.TRAIN-ALLisalargesetofautomaticallyjudged(thusnoisy)QApairs:asentenceisconsideredapositiveexampleifitmatchesthegoldanswerpatternforthecorrespondingquestion.TRAINisamuchsmallersubsetofTRAIN-ALL,containingpairsthataremanuallycorrectedforerrors.ManualjudgmentisproducedforDEVandTESTpairs,too.Foranswerextraction,Yaoetal.(2013a)addtoeachQApairthecorrectanswerchunk(s).ThegoldTRECpatternsareusedtofirstidentifyrele-vantchunksineachanswersentence.TRAIN,DEVandTESTarethenmanuallycorrectedforerrors.TheWangetal.(2007)datasetalsocomeswithPOS/DEP/NERtagsforeachsentence.TheyusetheMXPOSTtagger(Ratnaparkhi,1996)forPOStagging,theMSTParser(McDonaldetal.,2005)togeneratetypeddependencytrees,andtheBBNIden-tifinder(Bikeletal.,1999)forNERtagging.Al-thoughwehaveaccesstoastate-of-the-artinforma-tionpipelinethatproducesbettertags,thispaperaimstostudytheeffectoftheproposedmodelsandofourfeaturesonsystemperformance,ratherthanonad-ditionalvariables;por lo tanto,tosupportcomparisonwithpriorwork,werelyonthetagsprovidedwiththedatasetforallourexperiments.6.2AnswerSentenceRankingWeadoptthestandardevaluationprocedureandmet-ricsforQArankersreportedintheliterature.6.2.1EvaluationMetricsOurmetricsforrankingareMeanAveragePre-cision(MAP)andMeanReciprocalRank(MRR).Herewedefinebothintermsofsimplermetrics.PrecisionatK.GivenaquestionQandasetofcandidateanswersentences{S(1),…,S(norte)},lettheoutputofarankerbe[R(1),…,R(norte)],sothateachR(i)∈{S(1),…,S(norte)}andthepredictedrankofR(i)ishigherthanthepredictedrankofR(j)when-everi
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ModelMAP%MRR%TRAINShnarch(2013)68.6075.40Yihetal.(2013)70.9277.00Yuetal.(2014)70.5878.00Severyn&Moschitti(2015)73.2979.62OurStandaloneModel76.0583.99OurJointProbabilisticModel81.5989.09OurStackedModel80.7786.85TRAIN-ALLYuetal.(2014)71.1378.46Severyn&Moschitti(2015)74.5980.78OurStandaloneModel75.6883.09OurJointProbabilisticModel84.9591.95OurStackedModel82.5690.69Table3:Answersentencerankingresults.Unlikethestandalonemodel,thejointmodelsalsobenefitfromtheadditionalnoisyexamplesinTRAIN-ALL.Theseresultssupportthecentralargumentofthispaperthatjointmodelingisabetterapproachtoanswersentenceranking.6.3AnswerExtractionWefollowtheprocedurereportedinpriorwork(Yaoetal.,2013a;SeverynandMoschitti,2013)toevalu-atetheanswerchunksextractedbythesystem.6.3.1EvaluationMetricsPrecision.Givenasetofquestions,theprecisionofananswerextractionsystemistheproportionofitsextractedanswersthatarecorrect(i.e.matchthecorrespondinggoldregexppattern).Recall.Recallistheproportionofquestionsforwhichthesystemextractedacorrectanswer.F1Score.TheF1scoreistheharmonicmeanofprecisionandrecall.Itcapturesthesystem’saccuracyandcoverageinasinglemetric.6.3.2SetupFollowingpriorwork,nosotros(1)retainthe89ques-tionsintheWangetal.(2007)TESTsetthathaveatleastonecorrectanswer,y(2)trainonlywithchunksincorrectanswersentencestoavoidextremebiastowardsfalselabels(boththestandaloneextrac-tionmodelandstage2ofthestackedmodel).AsinModelP%R%F1%TRAINYaoetal.(2013a)55.253.954.5Severyn&Moschitti(2013)66.266.266.2OurStandaloneModel62.962.962.9OurJointProbabilisticModel69.769.769.7OurStackedModel62.962.962.9TRAIN-ALLYaoetal.(2013a)63.662.963.3Severyn&Moschitti(2013)70.870.870.8OurStandaloneModel70.870.870.8OurJointProbabilisticModel76.476.476.4OurStackedModel73.073.073.0Table4:AnswerextractionresultsontheWangetal.(2007)testset.ranking,weuseScikit-learnforlogisticregressionandsettheregularizationparameterCusingDEV.6.3.3ResultsTable4showsperformancesofourextractionmod-elsontheWangetal.TESTset.Thejointproba-bilisticmodeldemonstratestopperformanceforbothTRAINandTRAIN-ALL.WithTRAIN-ALL,itcor-rectlyanswers68ofthe89testquestions(5morethanthepreviousbestmodelofSeverynandMos-chitti(2013)).Thestackedmodelalsoperformswellwiththelargertrainingset.Again,theseresultssup-portthecentralclaimofthepaperthatanswerextrac-tioncanbemadebetterthroughjointmodeling.Table5showsperformancesofourstandaloneandjointprobabilisticmodels(trainedonTRAIN-ALL)ondifferentTESTquestiontypes.Thejointmodelisthebetterofthetwoacrosstypes,achievinggoodQuestionTypeCountSTJPwhat3751.456.8when19100.0100.0where11100.090.9who/whom1060.070.0why10.00.0howmany977.8100.0howlong250.0100.0Table5:F1%oftheSTandaloneandtheJointProbabilisticextractionmodelacrossquestiontypes.
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QuestionCandidateAnswerSentenceSTJPHowmanyyearswasJackWelchwithGE?“SixSigmahasgalvanizedourcompanywithanintensitythelikesofwhichIhaveneverseeninmy40yearsatGE,”saidJohnWelch,chairmanofGeneralElectric..517.113SoferventaproselytizerisWelchthatGEhasspentthreeyearsandmorethan$1billiontoconvertallofitsdivisionstotheSixSigmafaith..714.090WhatkindofshipistheLibertyBell7?Newportplanstoretrievetherecoveryvesselfirst,thengoafterLibertyBell7,theonlyU.S.mannedspacecraftlostafterasuccessfulmission..838.278“Itwillbeabigrelief”oncethecapsuleisaboardship,CurtNewportsaidbeforesettingsailThursday..388.003Table6:ScorescomputedbytheSTandaloneandtheJointProbabilisticmodelforcandidatechunks(boldfaced)infour(Wangetal.,2007)testsentences.Jointmodelscoresfornon-answerchunks(rows2and4)aremuchlower.resultsonallquestiontypesexceptwhat.Aparticularlychallengingsubtypeofwhatques-tionsarewhatbequestions,answerstowhichoftengobeyondNPchunkboundaries.Ahuman-extractedanswertothequestion“WhatisMuslimBrother-hood’sgoal?”intheWangetal.corpus(2007),forexample,is“advocatesturningEgyptintoastrictMuslimstatebypoliticalmeans.”Whatingeneralisneverthelessthemostdifficultquestiontype,sinceunlikequestionslikewhoorwhen,answersdonothavestrictcategories(e.g.,afixedsetofNERtags).6.3.4QualitativeAnalysisWecloselyexamineQApairsforwhichthejointprobabilisticmodelextractsacorrectanswerchunkbutthestandalonemodeldoesnot.Table6showstwosuchquestions,withtwocandidateanswersentencesforeach.Candidateanswerchunksareboldfaced.Forthefirstquestion,onlythesentenceinrow1containsananswer.Thestandalonemodelassignsahigherscoretothenon-answerchunkinrow2,buttheuseofsentence-levelfeaturesenablesthejointmodeltoidentifythemorerelevantchunkinrow1.Notethatthejointmodelscore,beingaproductoftwoprobabilities,isalwayslowerthanthestandalonemodelscore.However,onlytherelativescoremattersinthiscase,asthechunkwiththehighestoverallscoreiseventuallyselectedforextraction.Forthesecondquestion,bothmodelscomputealowerscoreforthenon-answerchunk“CurtNewport”thantheanswerchunk“mannedspacecraft”.How-ever,theincorrectchunkappearsinseveralcandidateanswersentences(notshownhere),resultinginaModelP%R%F1%Yaoetal.(2013C)35.417.223.1OurJointProbabilisticModel83.883.883.8Table7:PerformancesoftwojointextractionmodelsontheYaoetal.(2013C)testset.highoverallscoreforthestandalonemodel(Algo-rithm1:steps7and8).Thejointmodelassignsamuchlowerscoretoeachinstanceofthischunkduetoweaksentence-levelevidence,eventuallyresultingintheextractionofthecorrectchunk.6.3.5ASecondExtractionDatasetYaoetal.(2013C)reportanextractiondatasetcon-taining99testquestions,derivedfromtheMIT109testcollection(LinandKatz,2006)ofTRECpairs.Eachquestioninthisdatasethas10candidatean-swersentences.Wecomparetheperformanceofourjointprobabilisticmodelwiththatoftheirextractionmodel,whichextractsanswersfromtopcandidatesentencesidentifiedbytheircoupledranker(Sec-tion2.3).4Modelsaretrainedontheirtrainingsetof2,205questionsand22,043candidateQApairs.AsshowninTable7,ourmodeloutperformstheYaoetal.modelbyasurprisinglylargemargin,correctlyanswering83ofthe99testquestions.Interestingly,ourstandalonemodelextractssixmorecorrectanswersinthisdatasetthanthejoint4Wecomparewithonlytheirextractionmodel,asthelargerrankingdatasetisnotavailableanymore.Precisionandrecallarereportedathttp://cs.jhu.edu/˜xuchen/packages/jacana-ir-acl2013-data-results.tar.bz2.
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CandidateAnswerSentenceSTJPAnotherperkisgettingtoworkwithhisson,BarryVanDyke,whohasaregularroleasDetectiveSteveSloanon“Diagnosis”..861.338ThisisonlythethirdtimeinschoolhistorytheRaidershavebegunaseason6-0andthefirstsince1976,whenSteveSloan,inhissecondseasonascoach,ledthemtoan8-0startand10-2overallrecord..494.010HealsorepresentedseveralAlabamacoaches,includingRayPerkins,BillCurry,SteveSloanandWimpSanderson..334.007BartStarr,JoeNamath,KenStabler,SteveSloan,ScottHunterandWalterLewisarebutafewofthelegendsonthewalloftheCrimsonTidequarterbackscoach..334.009Table8:ScorescomputedbytheSTandaloneandtheJointProbabilisticmodelforNPchunks(boldfaced)infourYaoetal.(2013C)testsentencesforthequestion:Whoisthedetectiveon‘DiagnosisMurder’?Thestandalonemodelassignshighprobabilitiestonon-answerchunksinthelastthreesentences,subsequentlycorrectedbythejointmodel.model.Acloseexaminationrevealsthatinallsixcases,thisiscausedbythepresenceofcorrectan-swerchunksinnon-answersentences.Table8showsanexample,wherethecorrectanswerchunk“SteveSloan”appearsinallfourcandidatesentences,ofwhichonlythefirstisactuallyrelevanttotheques-tion.Thestandalonemodelassignshighscorestoallfourinstancesandasaresultobservesahighoverallscoreforthechunk.Thejointmodel,por otro lado,recognizesthefalsepositives,andcon-sequentlyobservesasmalleroverallscoreforthechunk.However,thisdesiredbehavioreventuallyresultsinawrongextraction.Theseresultshavekeyimplicationsfortheevaluationofanswerextractionsystems:metricsthatassessperformanceonindivid-ualQApairscanenablefiner-grainedevaluationthanwhatend-to-endextractionmetricsoffer.7DiscussionOurtwo-stepapproachtojointmodeling,consist-ingofconstructingseparatemodelsforrankingandextractionfirstandthencouplingtheirpredictions,offersatleasttwoadvantages.First,predictionsfromanygivenpairofrankingandextractionsystemscanbecombined,sincesuchsystemsmustcomputeascoreforaQApairorananswerchunkinordertodifferentiateamongcandidates.CouplingoftherankingandextractionsystemsofYaoetal.(2013a)andSeverynandMoschitti(2013),forexample,isstraightforwardwithinourframework.Second,thisapproachsupportstheuseoftask-appropriatetrainingdataforrankingandextraction,whichcanprovidekeyadvantage.Forexample,whileanswersentencerankingsystemsusebothcorrectandincorrectcan-didateanswersentencesformodeltraining,existinganswerextractionsystemsdiscardthelatterinordertomaintaina(relatively)balancedclassdistribution(Yaoetal.,2013a;SeverynandMoschitti,2013).Throughtheseparationoftherankingandextrac-tionmodelsduringtraining,ourapproachnaturallysupportssuchtask-specificsamplingoftrainingdata.ApotentiallylimitingfactorinourextractionmodelistheassumptionthatanswersarealwaysexpressedneatlyinNPchunks.Whilemodelsthatmakenosuchassumptionexist(e.g.,theCRFmodelofYaoetal.(2013a)),extractionoflonganswers(suchastheonediscussedinSection6.3.3)isstilldifficultinpracticeduetotheirunconstrainednature.8ConclusionsandFutureWorkWepresentajointmodelfortheimportantQAtasksofanswersentencerankingandanswerextraction.Byexploitingtheinterconnectednatureofthetwotasks,ourmodeldemonstratessubstantialperfor-manceimprovementsoverpreviousbestsystemsforboth.Additionally,ourrankingmodelappliesrecentadvancesinthecomputationofshorttextsimilaritytoQA,providingstrongersimilarityfeatures.Anobviousdirectionforfutureworkistheinclu-sionofnewfeaturesforeachtask.Answersentenceranking,forexample,canbenefitfromphrasalalign-mentandlong-distancecontextrepresentation.An-swerextractionforwhatquestionscanbemadebetterusingalexicalanswertypefeature,orworldknowl-
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