ARC , TREC, GARD , MLBioMedLAT 
EXTRACTIVE
ARC, TREC, GARD, MLBioMedLAT
EXTRACTIVE
Apart from term frequency methods, question classification methods developed on one dataset generally do not exhibit strong transfer performance to other datasets BIBREF3 . While BERT-QC achieves large gains over existing methods on the ARC dataset, here we demonstrate that BERT-QC also matches state-of-the-art performance on TREC BIBREF6 , while surpassing state-of-the-art performance on the GARD corpus of consumer health questions BIBREF3 and MLBioMedLAT corpus of biomedical questions BIBREF4 . As such, BERT-QC is the first model to achieve strong performance across more than one question classification dataset.
Apart from term frequency methods, question classification methods developed on one dataset generally do not exhibit strong transfer performance to other datasets BIBREF3 . While BERT-QC achieves large gains over existing methods on the ARC dataset, here we demonstrate that BERT-QC also matches state-of-the-art performance on TREC BIBREF6 , while surpassing state-of-the-art performance on the GARD corpus of consumer health questions BIBREF3 and MLBioMedLAT corpus of biomedical questions BIBREF4 . As such, BERT-QC is the first model to achieve strong performance across more than one question classification dataset.
1908.05441
Introduction </s> Understanding Ġwhat Ġa Ġquestion Ġis Ġasking Ġis Ġone Ġof Ġthe Ġfirst Ġsteps Ġthat Ġhumans Ġuse Ġto Ġwork Ġtowards Ġan Ġanswer . ĠIn Ġthe Ġcontext Ġof Ġquestion Ġanswering , Ġquestion Ġclassification Ġallows Ġautomated Ġsystems Ġto Ġintellig ently Ġtarget Ġtheir Ġinference Ġsystems Ġto Ġdomain - specific Ġsol vers Ġcapable Ġof Ġaddressing Ġspecific Ġkinds Ġof Ġquestions Ġand Ġproblem Ġsolving Ġmethods Ġwith Ġhigh Ġconfidence Ġand Ġanswer Ġaccuracy ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ. </s> To Ġdate , Ġquestion Ġclassification Ġhas Ġprimarily Ġbeen Ġstudied Ġin Ġthe Ġcontext Ġof Ġopen - domain ĠT REC Ġquestions ĠB IB REF 2 Ġ, Ġwith Ġsmaller Ġrecent Ġdatasets Ġavailable Ġin Ġthe Ġbiomedical ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġand Ġeducation ĠB IB REF 5 Ġdomains . ĠThe Ġopen - domain ĠT REC Ġquestion Ġcorpus Ġis Ġa Ġset Ġof Ġ5 , 952 Ġshort Ġfact oid Ġquestions Ġpaired Ġwith Ġa Ġtax onomy Ġdeveloped Ġby ĠLi Ġand ĠRoth ĠB IB REF 6 Ġthat Ġincludes Ġ6 Ġcoarse Ġanswer Ġtypes Ġ( such Ġas Ġentities , Ġlocations , Ġand Ġnumbers ), Ġand Ġ50 Ġfine - gr ained Ġtypes Ġ( e . g . Ġspecific Ġkinds Ġof Ġentities , Ġsuch Ġas Ġanimals Ġor Ġvehicles ). ĠWhile Ġa Ġwide Ġvariety Ġof Ġsynt actic , Ġsemantic , Ġand Ġother Ġfeatures Ġand Ġclassification Ġmethods Ġhave Ġbeen Ġapplied Ġto Ġthis Ġtask , Ġculminating Ġin Ġnear - perfect Ġclassification Ġperformance ĠB IB REF 7 Ġ, Ġrecent Ġwork Ġhas Ġdemonstrated Ġthat ĠQC Ġmethods Ġdeveloped Ġon ĠT REC Ġquestions Ġgenerally Ġfail Ġto Ġtransfer Ġto Ġdatasets Ġwith Ġmore Ġcomplex Ġquestions Ġsuch Ġas Ġthose Ġin Ġthe Ġbiomedical Ġdomain ĠB IB REF 3 Ġ, Ġlikely Ġdue Ġin Ġpart Ġto Ġthe Ġsimplicity Ġand Ġsynt actic Ġregular ity Ġof Ġthe Ġquestions , Ġand Ġthe Ġability Ġfor Ġsimpler Ġterm - frequency Ġmodels Ġto Ġachieve Ġnear - ce iling Ġperformance ĠB IB REF 8 Ġ. ĠIn Ġthis Ġwork Ġwe Ġexplore Ġquestion Ġclassification Ġin Ġthe Ġcontext Ġof Ġmultiple Ġchoice Ġscience Ġexams . ĠStandard ized Ġscience Ġexams Ġhave Ġbeen Ġproposed Ġas Ġa Ġchallenge Ġtask Ġfor Ġquestion Ġanswering ĠB IB REF 9 Ġ, Ġas Ġmost Ġquestions Ġcontain Ġa Ġvariety Ġof Ġchallenging Ġinference Ġproblems ĠB IB REF 10 Ġ, ĠB IB REF 11 Ġ, Ġrequire Ġdetailed Ġscientific Ġand Ġcommon - sense Ġknowledge Ġto Ġanswer Ġand Ġexplain Ġthe Ġreasoning Ġbehind Ġthose Ġanswers ĠB IB REF 12 Ġ, Ġand Ġquestions Ġare Ġoften Ġembedded Ġin Ġcomplex Ġexamples Ġor Ġother Ġdistract ors . ĠQuestion Ġclassification Ġtax onom ies Ġand Ġannotation Ġare Ġdifficult Ġand Ġexpensive Ġto Ġgenerate , Ġand Ġbecause Ġof Ġthe Ġun availability Ġof Ġthis Ġdata , Ġto Ġdate Ġmost Ġmodels Ġfor Ġscience Ġquestions Ġuse Ġone Ġor Ġa Ġsmall Ġnumber Ġof Ġgeneric Ġsol vers Ġthat Ġperform Ġlittle Ġor Ġno Ġquestion Ġdecom position ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ, ĠB IB REF 15 Ġ, ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ. ĠOur Ġlong - term Ġinterest Ġis Ġin Ġdeveloping Ġmethods Ġthat Ġintellig ently Ġtarget Ġtheir Ġinf erences Ġto Ġgenerate Ġboth Ġcorrect Ġanswers Ġand Ġcompelling Ġhuman - readable Ġexplanations Ġfor Ġthe Ġreasoning Ġbehind Ġthose Ġanswers . ĠThe Ġlack Ġof Ġtargeted Ġsolving ĠâĢĵ Ġusing Ġthe Ġsame Ġmethods Ġfor Ġinfer ring Ġanswers Ġto Ġspatial Ġquestions Ġabout Ġplanetary Ġmotion , Ġchemical Ġquestions Ġabout Ġphotos ynthesis , Ġand Ġelectrical Ġquestions Ġabout Ġcircuit Ġcontinuity ĠâĢĵ Ġis Ġa Ġsubstantial Ġbarrier Ġto Ġincreasing Ġperformance Ġ( see ĠFigure ĠFIG REF 1 Ġ). </s> To Ġaddress Ġthis Ġneed Ġfor Ġdeveloping Ġmethods Ġof Ġtarget ted Ġinference , Ġthis Ġwork Ġmakes Ġthe Ġfollowing Ġcontributions : </s> Related Ġwork </s> Question Ġclassification Ġtypically Ġmakes Ġuse Ġof Ġa Ġcombination Ġof Ġsynt actic , Ġsemantic , Ġsurface , Ġand Ġembed ding Ġmethods . ĠSynt actic Ġpatterns ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġ, ĠB IB REF 21 Ġand Ġsynt actic Ġdependencies ĠB IB REF 3 Ġhave Ġbeen Ġshown Ġto Ġimprove Ġperformance , Ġwhile Ġsynt act ically Ġor Ġsem antically Ġimportant Ġwords Ġare Ġoften Ġexpanding Ġusing ĠWord net Ġhyper ny ms Ġor ĠUnified ĠMedical ĠLanguage ĠSystem Ġcategories Ġ( for Ġthe Ġmedical Ġdomain ) Ġto Ġhelp Ġmitigate Ġsp arsity ĠB IB REF 22 Ġ, ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġ. ĠKey word Ġidentification Ġhelps Ġidentify Ġspecific Ġterms Ġuseful Ġfor Ġclassification ĠB IB REF 25 Ġ, ĠB IB REF 3 Ġ, ĠB IB REF 26 Ġ. ĠSimilarly , Ġnamed Ġentity Ġrecogn izers ĠB IB REF 6 Ġ, ĠB IB REF 27 Ġor Ġlists Ġof Ġsem antically Ġrelated Ġwords ĠB IB REF 6 Ġ, ĠB IB REF 24 Ġcan Ġalso Ġbe Ġused Ġto Ġestablish Ġbroad Ġtopics Ġor Ġentity Ġcategories Ġand Ġmitigate Ġsp arsity , Ġas Ġcan Ġword Ġembed d ings ĠB IB REF 28 Ġ, ĠB IB REF 29 Ġ. ĠHere , Ġwe Ġempir ically Ġdemonstrate Ġmany Ġof Ġthese Ġexisting Ġmethods Ġdo Ġnot Ġtransfer Ġto Ġthe Ġscience Ġdomain . </s> The Ġhighest Ġperforming Ġquestion Ġclassification Ġsystems Ġtend Ġto Ġmake Ġuse Ġof Ġcustomized Ġrule - based Ġpattern Ġmatching ĠB IB REF 30 Ġ, ĠB IB REF 7 Ġ, Ġor Ġa Ġcombination Ġof Ġrule - based Ġand Ġmachine Ġlearning Ġapproaches ĠB IB REF 19 Ġ, Ġat Ġthe Ġexpense Ġof Ġincreased Ġmodel Ġconstruction Ġtime . ĠA Ġrecent Ġemphasis Ġon Ġlearned Ġmethods Ġhas Ġshown Ġa Ġlarge Ġset Ġof ĠCNN ĠB IB REF 29 Ġand ĠL ST M ĠB IB REF 8 Ġvariants Ġachieve Ġsimilar Ġaccuracy Ġon ĠT REC Ġquestion Ġclassification , Ġwith Ġthese Ġmodels Ġexhibiting Ġat Ġbest Ġsmall Ġgains Ġover Ġsimple Ġterm Ġfrequency Ġmodels . ĠThese Ġrecent Ġdevelopments Ġecho Ġthe Ġobservations Ġof ĠRoberts Ġet Ġal . ĠB IB REF 3 Ġ, Ġwho Ġshowed Ġthat Ġexisting Ġmethods Ġbeyond Ġterm Ġfrequency Ġmodels Ġfailed Ġto Ġgeneral ize Ġto Ġmedical Ġdomain Ġquestions . ĠHere Ġwe Ġshow Ġthat Ġstrong Ġperformance Ġacross Ġmultiple Ġdatasets Ġis Ġpossible Ġusing Ġa Ġsingle Ġlearned Ġmodel . </s> Due Ġto Ġthe Ġcost Ġinvolved Ġin Ġtheir Ġconstruction , Ġquestion Ġclassification Ġdatasets Ġand Ġclassification Ġtax onom ies Ġtend Ġto Ġbe Ġsmall , Ġwhich Ġcan Ġcreate Ġmethodological Ġchallenges . ĠRoberts Ġet Ġal . ĠB IB REF 3 Ġgenerated Ġthe Ġnext - largest Ġdataset Ġfrom ĠT REC , Ġcontaining Ġ2 , 9 36 Ġconsumer Ġhealth Ġquestions Ġclassified Ġinto Ġ13 Ġquestion Ġcategories . ĠMore Ġrecently , ĠWas im Ġet Ġal . ĠB IB REF 4 Ġgenerated Ġa Ġsmall Ġcorpus Ġof Ġ780 Ġbiomedical Ġdomain Ġquestions Ġorganized Ġinto Ġ88 Ġcategories . ĠIn Ġthe Ġeducation Ġdomain , ĠG ode a Ġet Ġal . ĠB IB REF 5 Ġcollected Ġa Ġset Ġof Ġ1 , 155 Ġclassroom Ġquestions Ġand Ġorganized Ġthese Ġinto Ġ16 Ġcategories . ĠTo Ġenable Ġa Ġdetailed Ġstudy Ġof Ġscience Ġdomain Ġquestion Ġclassification , Ġhere Ġwe Ġconstruct Ġa Ġlarge - scale Ġchallenge Ġdataset Ġthat Ġexceeds Ġthe Ġsize Ġand Ġclassification Ġspecificity Ġof Ġother Ġdatasets , Ġin Ġmany Ġcases Ġby Ġnearly Ġan Ġorder Ġof Ġmagnitude . </s> Questions Ġand ĠClassification ĠTax onomy </s> Questions : ĠWe Ġmake Ġuse Ġof Ġthe Ġ7 , 787 Ġscience Ġexam Ġquestions Ġof Ġthe ĠArist o ĠReason ing ĠChallenge Ġ( ARC ) Ġcorpus ĠB IB REF 31 Ġ, Ġwhich Ġcontains Ġstandardized Ġ3 rd Ġto Ġ9 th Ġgrade Ġscience Ġquestions Ġfrom Ġ12 ĠUS Ġstates Ġfrom Ġthe Ġpast Ġdecade . ĠEach Ġquestion Ġis Ġa Ġ4 - choice Ġmultiple Ġchoice Ġquestion . ĠSummary Ġstatistics Ġcomparing Ġthe Ġcomplexity Ġof ĠARC Ġand ĠT REC Ġquestions Ġare Ġshown Ġin ĠTable ĠT AB REF 5 Ġ. </s> Tax onomy : ĠStarting Ġwith Ġthe Ġsyll abus Ġfor Ġthe ĠNY ĠReg ents Ġexam , Ġwe Ġidentified Ġ9 Ġcoarse Ġquestion Ġcategories Ġ( Ast ron omy , ĠEarth ĠScience , ĠEnergy , ĠForces , ĠLife ĠScience , ĠMatter , ĠSafety , ĠScientific ĠMethod , ĠOther ), Ġthen Ġthrough Ġa Ġdata - driven Ġanalysis Ġof Ġ3 Ġexam Ġstudy Ġguides Ġand Ġthe Ġ3 , 370 Ġtraining Ġquestions , Ġexpanded Ġthe Ġtax onomy Ġto Ġinclude Ġ4 62 Ġfine - gr ained Ġcategories Ġacross Ġ6 Ġhierarchical Ġlevels Ġof Ġgran ularity . ĠThe Ġtax onomy Ġis Ġdesigned Ġto Ġallow Ġcategor izing Ġquestions Ġinto Ġbroad Ġcurriculum Ġtopics Ġat Ġit 's Ġco ars est Ġlevel , Ġwhile Ġlabels Ġat Ġfull Ġspecificity Ġseparate Ġquestions Ġinto Ġnarrow Ġproblem Ġdomains Ġsuitable Ġfor Ġtarget ted Ġinference Ġmethods . ĠBecause Ġof Ġits Ġsize , Ġa Ġsubset Ġof Ġthe Ġclassification Ġtax onomy Ġis Ġshown Ġin ĠTable ĠT AB REF 6 Ġ, Ġwith Ġthe Ġfull Ġtax onomy Ġand Ġclass Ġdefinitions Ġincluded Ġin Ġthe Ġsupplementary Ġmaterial . </s> An notation : ĠBecause Ġof Ġthe Ġcomplexity Ġof Ġthe Ġquestions , Ġit Ġis Ġpossible Ġfor Ġone Ġquestion Ġto Ġbridge Ġmultiple Ġcategories ĠâĢĵ Ġfor Ġexample , Ġa Ġwind Ġpower Ġgeneration Ġquestion Ġmay Ġspan Ġboth Ġrenewable Ġenergy Ġand Ġenergy Ġconversion . ĠWe Ġallow Ġup Ġto Ġ2 Ġlabels Ġper Ġquestion , Ġand Ġfound Ġthat Ġ16 % Ġof Ġquestions Ġrequired Ġmultiple Ġlabels . ĠEach Ġquestion Ġwas Ġindependently Ġannot ated Ġby Ġtwo Ġannot ators , Ġwith Ġthe Ġlead Ġannot ator Ġa Ġdomain Ġexpert Ġin Ġstandardized Ġexams . ĠAn not ators Ġfirst Ġindependently Ġannot ated Ġthe Ġentire Ġquestion Ġset , Ġthen Ġquestions Ġwithout Ġcomplete Ġagreement Ġwere Ġdiscussed Ġuntil Ġresolution . ĠBefore Ġresolution , Ġinter annot ator Ġagreement Ġ( Co hen 's ĠKappa ) Ġwas ĠIN LINE FORM 0 Ġ= Ġ0 . 58 Ġat Ġthe Ġfinest Ġlevel Ġof Ġgran ularity , Ġand ĠIN LINE FORM 1 Ġ= Ġ0 . 85 Ġwhen Ġconsidering Ġonly Ġthe Ġco ars est Ġ9 Ġcategories . ĠThis Ġis Ġconsidered Ġmoderate Ġto Ġstrong Ġagreement ĠB IB REF 32 Ġ. ĠBased Ġon Ġthe Ġresults Ġof Ġour Ġerror Ġanalysis Ġ( see ĠSection ĠSEC REF 21 Ġ), Ġwe Ġestimate Ġthe Ġoverall Ġaccuracy Ġof Ġthe Ġquestion Ġclassification Ġlabels Ġafter Ġresolution Ġto Ġbe Ġapproximately Ġ96 %. ĠWhile Ġthe Ġfull Ġtax onomy Ġcontains Ġ4 62 Ġfine - gr ained Ġcategories Ġderived Ġfrom Ġboth Ġstandardized Ġquestions , Ġstudy Ġguides , Ġand Ġexam Ġsyll abi , Ġwe Ġobserved Ġonly Ġ406 Ġof Ġthese Ġcategories Ġare Ġtested Ġin Ġthe ĠARC Ġquestion Ġset . </s> Question ĠClassification Ġon ĠScience ĠEx ams </s> We Ġidentified Ġ5 Ġcommon Ġmodels Ġin Ġprevious Ġwork Ġprimarily Ġintended Ġfor Ġlearned Ġclass ifiers Ġrather Ġthan Ġhand - crafted Ġrules . ĠWe Ġadapt Ġthese Ġmodels Ġto Ġa Ġmulti - label Ġhierarchical Ġclassification Ġtask Ġby Ġtraining Ġa Ġseries Ġof Ġone - vs - all Ġbinary Ġclass ifiers ĠB IB REF 34 Ġ, Ġone Ġfor Ġeach Ġlabel Ġin Ġthe Ġtax onomy . ĠWith Ġthe Ġexception Ġof Ġthe ĠCNN Ġand ĠB ERT Ġmodels , Ġfollowing Ġprevious Ġwork ĠB IB REF 19 Ġ, ĠB IB REF 3 Ġ, ĠB IB REF 8 Ġwe Ġmake Ġuse Ġof Ġan ĠS VM Ġclass ifier Ġusing Ġthe ĠLI BS v M Ġframework ĠB IB REF 35 Ġwith Ġa Ġlinear Ġkernel . ĠModels Ġare Ġtrained Ġand Ġevaluated Ġfrom Ġcoarse Ġto Ġfine Ġlevels Ġof Ġtax onomic Ġspecificity . ĠAt Ġeach Ġlevel Ġof Ġtax onomic Ġevaluation , Ġa Ġset Ġof Ġnon - over l apping Ġconfidence Ġscores Ġfor Ġeach Ġbinary Ġclass ifier Ġare Ġgenerated Ġand Ġsorted Ġto Ġproduce Ġa Ġlist Ġof Ġranked Ġlabel Ġpredictions . ĠWe Ġevaluate Ġthese Ġranks Ġusing ĠMean ĠAverage ĠPrecision ĠB IB REF 36 Ġ. ĠARC Ġquestions Ġare Ġevaluated Ġusing Ġthe Ġstandard Ġ3 , 370 Ġquestions Ġfor Ġtraining , Ġ8 69 Ġfor Ġdevelopment , Ġand Ġ3 , 548 Ġfor Ġtesting . </s> N - gram s , ĠPOS , ĠHier arch ical Ġfeatures : ĠA Ġbaseline Ġbag - of - words Ġmodel Ġincorporating Ġboth Ġtagged Ġand Ġunt agged Ġun ig rams Ġand Ġbig ams . ĠWe Ġalso Ġimplement Ġthe Ġhierarchical Ġclassification Ġfeature Ġof ĠLi Ġand ĠRoth ĠB IB REF 6 Ġ, Ġwhere Ġfor Ġa Ġgiven Ġquestion , Ġthe Ġoutput Ġof Ġthe Ġclass ifier Ġat Ġco arser Ġlevels Ġof Ġgran ularity Ġserves Ġas Ġinput Ġto Ġthe Ġclass ifier Ġat Ġthe Ġcurrent Ġlevel Ġof Ġgran ularity . </s> D epend encies : ĠBig rams Ġof ĠStanford Ġdependencies ĠB IB REF 37 Ġ. ĠFor Ġeach Ġword , Ġwe Ġcreate Ġone Ġunl abel ed Ġbig ram Ġfor Ġeach Ġoutgoing Ġlink Ġfrom Ġthat Ġword Ġto Ġit 's Ġdependency ĠB IB REF 20 Ġ, ĠB IB REF 3 Ġ. </s> Question ĠExpansion Ġwith ĠHyper ny ms : ĠWe Ġperform Ġhyper ny m Ġexpansion ĠB IB REF 22 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 3 Ġby Ġincluding ĠWord Net Ġhyper ny ms ĠB IB REF 38 Ġfor Ġthe Ġroot Ġdependency Ġword , Ġand Ġwords Ġon Ġit 's Ġdirect Ġoutgoing Ġlinks . ĠWord Net Ġsense Ġis Ġidentified Ġusing ĠLes k Ġword - sense Ġdis amb ig uation ĠB IB REF 39 Ġ, Ġusing Ġquestion Ġtext Ġfor Ġcontext . ĠWe Ġimplement Ġthe Ġhe uristic Ġof ĠVan - tu Ġet Ġal . ĠB IB REF 24 Ġ, Ġwhere Ġmore Ġdistant Ġhyper ny ms Ġreceive Ġless Ġweight . </s> Ess ential ĠTerms : ĠThough Ġnot Ġpreviously Ġreported Ġfor ĠQC , Ġwe Ġmake Ġuse Ġof Ġun ig rams Ġof Ġkeywords Ġextracted Ġusing Ġthe ĠScience ĠExam ĠEssential ĠTerm ĠExtract or Ġof ĠKh ash abi Ġet Ġal . ĠB IB REF 26 Ġ. ĠFor Ġeach Ġkeyword , Ġwe Ġcreate Ġone Ġbinary Ġun ig ram Ġfeature . </s> CNN : ĠKim ĠB IB REF 28 Ġdemonstrated Ġnear Ġstate - of - the - art Ġperformance Ġon Ġa Ġnumber Ġof Ġsentence Ġclassification Ġtasks Ġ( including ĠT REC Ġquestion Ġclassification ) Ġby Ġusing Ġpre - trained Ġword Ġembed d ings ĠB IB REF 40 Ġas Ġfeature Ġextract ors Ġin Ġa ĠCNN Ġmodel . ĠLei Ġet Ġal . ĠB IB REF 29 Ġshowed Ġthat Ġ10 ĠCNN Ġvariants Ġperform Ġwithin Ġ+/- 2 % Ġof ĠKim 's ĠB IB REF 28 Ġmodel Ġon ĠT REC ĠQC . ĠWe Ġreport Ġperformance Ġof Ġour Ġbest ĠCNN Ġmodel Ġbased Ġon Ġthe ĠMP - CNN Ġarchitecture Ġof ĠRao Ġet Ġal . ĠB IB REF 41 Ġ, Ġwhich Ġworks Ġto Ġestablish Ġthe Ġsimilarity Ġbetween Ġquestion Ġtext Ġand Ġthe Ġdefinition Ġtext Ġof Ġthe Ġquestion Ġclasses . ĠWe Ġadapt Ġthe ĠMP - CNN Ġmodel , Ġwhich Ġuses Ġa ĠâĢ ľ Si ames e âĢ Ŀ Ġstructure ĠB IB REF 33 Ġ, Ġto Ġcreate Ġseparate Ġrepresentations Ġfor Ġboth Ġthe Ġquestion Ġand Ġthe Ġquestion Ġclass . ĠThe Ġmodel Ġthen Ġmakes Ġuse Ġof Ġa Ġtriple Ġranking Ġloss Ġfunction Ġto Ġminimize Ġthe Ġdistance Ġbetween Ġthe Ġrepresentations Ġof Ġquestions Ġand Ġthe Ġcorrect Ġclass Ġwhile Ġsimultaneously Ġmaxim ising Ġthe Ġdistance Ġbetween Ġquestions Ġand Ġincorrect Ġclasses . ĠWe Ġoptimize Ġthe Ġnetwork Ġusing Ġthe Ġmethod Ġof ĠTu ĠB IB REF 42 Ġ. </s> BER T - Q C Ġ( This Ġwork ): ĠWe Ġmake Ġuse Ġof ĠB ERT ĠB IB REF 43 Ġ, Ġa Ġlanguage Ġmodel Ġusing Ġbid irection al Ġenc oder Ġrepresentations Ġfrom Ġtransform ers , Ġin Ġa Ġsentence - class ification Ġconfiguration . ĠAs Ġthe Ġoriginal Ġsettings Ġof ĠB ERT Ġdo Ġnot Ġsupport Ġmulti - label Ġclassification Ġscenarios , Ġand Ġtraining Ġa Ġseries Ġof Ġ406 Ġbinary Ġclass ifiers Ġwould Ġbe Ġcomput ationally Ġexpensive , Ġwe Ġuse Ġthe Ġduplication Ġmethod Ġof ĠTs ou m ak as Ġet Ġal . ĠB IB REF 34 Ġwhere Ġwe Ġenumer ate Ġmulti - label Ġquestions Ġas Ġmultiple Ġsingle - label Ġinstances Ġduring Ġtraining Ġby Ġdupl icating Ġquestion Ġtext , Ġand Ġassigning Ġeach Ġinstance Ġone Ġof Ġthe Ġmultiple Ġlabels . ĠEvaluation Ġfollows Ġthe Ġstandard Ġprocedure Ġwhere Ġwe Ġgenerate Ġa Ġlist Ġof Ġranked Ġclass Ġpredictions Ġbased Ġon Ġclass Ġprobabilities , Ġand Ġuse Ġthis Ġto Ġcalculate ĠMean ĠAverage ĠPrecision Ġ( MAP ) Ġand ĠPrecision @ 1 Ġ( P @ 1 ). ĠAs Ġshown Ġin ĠTable ĠT AB REF 7 Ġ, Ġthis ĠB ERT - Q C Ġmodel Ġachieves Ġour Ġbest Ġquestion Ġclassification Ġperformance , Ġsignificantly Ġexceeding Ġbaseline Ġperformance Ġon ĠARC Ġby Ġ0 . 12 ĠMAP Ġand Ġ13 . 5 % ĠP @ 1 . </s> Compar ison Ġwith ĠBench mark ĠDat as ets </s> Apart Ġfrom Ġterm Ġfrequency Ġmethods , Ġquestion Ġclassification Ġmethods Ġdeveloped Ġon Ġone Ġdataset Ġgenerally Ġdo Ġnot Ġexhibit Ġstrong Ġtransfer Ġperformance Ġto Ġother Ġdatasets ĠB IB REF 3 Ġ. ĠWhile ĠB ERT - Q C Ġachieves Ġlarge Ġgains Ġover Ġexisting Ġmethods Ġon Ġthe ĠARC Ġdataset , Ġhere Ġwe Ġdemonstrate Ġthat ĠB ERT - Q C Ġalso Ġmatches Ġstate - of - the - art Ġperformance Ġon ĠT REC ĠB IB REF 6 Ġ, Ġwhile Ġsurpass ing Ġstate - of - the - art Ġperformance Ġon Ġthe ĠG ARD Ġcorpus Ġof Ġconsumer Ġhealth Ġquestions ĠB IB REF 3 Ġand ĠMLB io Med L AT Ġcorpus Ġof Ġbiomedical Ġquestions ĠB IB REF 4 Ġ. ĠAs Ġsuch , ĠB ERT - Q C Ġis Ġthe Ġfirst Ġmodel Ġto Ġachieve Ġstrong Ġperformance Ġacross Ġmore Ġthan Ġone Ġquestion Ġclassification Ġdataset . </s> T REC Ġquestion Ġclassification Ġis Ġdivided Ġinto Ġseparate Ġcoarse Ġand Ġfine - gr ained Ġtasks Ġcentered Ġaround Ġinfer ring Ġthe Ġexpected Ġanswer Ġtypes Ġof Ġshort Ġopen - domain Ġfact oid Ġquestions . ĠT REC - 6 Ġincludes Ġ6 Ġcoarse Ġquestion Ġclasses Ġ( ab bre viation , Ġentity , Ġdescription , Ġhuman , Ġlocation , Ġnumeric ), Ġwhile ĠT REC - 50 Ġexpands Ġthese Ġinto Ġ50 Ġmore Ġfine - gr ained Ġtypes . ĠT REC Ġquestion Ġclassification Ġmethods Ġcan Ġbe Ġdivided Ġinto Ġthose Ġthat Ġlearn Ġthe Ġquestion Ġclassification Ġtask , Ġand Ġthose Ġthat Ġmake Ġuse Ġof Ġeither Ġhand - crafted Ġor Ġsemi - aut om ated Ġsynt actic Ġor Ġsemantic Ġextraction Ġrules Ġto Ġinfer Ġquestion Ġclasses . ĠTo Ġdate , Ġthe Ġbest Ġreported Ġaccuracy Ġfor Ġlearned Ġmethods Ġis Ġ98 . 0 % Ġby ĠXia Ġet Ġal . ĠB IB REF 8 Ġfor ĠT REC - 6 , Ġand Ġ91 . 6 % Ġby ĠVan - tu Ġet Ġal . ĠB IB REF 24 Ġfor ĠT REC - 50 . ĠMad ab ushi Ġet Ġal . ĠB IB REF 7 Ġachieve Ġthe Ġhighest Ġto - date Ġperformance Ġon ĠT REC - 50 Ġat Ġ97 . 2 %, Ġusing Ġrules Ġthat Ġleverage Ġthe Ġstrong Ġsynt actic Ġregular ities Ġin Ġthe Ġshort ĠT REC Ġfact oid Ġquestions . </s> We Ġcompare Ġthe Ġperformance Ġof ĠB ERT - Q C Ġwith Ġrecently Ġreported Ġperformance Ġon Ġthis Ġdataset Ġin ĠTable ĠT AB REF 11 Ġ. ĠB ERT - Q C Ġachieves Ġstate - of - the - art Ġperformance Ġon Ġfine - gr ained Ġclassification Ġ( T REC - 50 ) Ġfor Ġa Ġlearned Ġmodel Ġat Ġ92 . 0 % Ġaccuracy , Ġand Ġnear Ġstate - of - the - art Ġperformance Ġon Ġcoarse Ġclassification Ġ( T REC - 6 ) Ġat Ġ96 . 2 % Ġaccuracy . </s> Because Ġof Ġthe Ġchallenges Ġwith Ġcollecting Ġbiomedical Ġquestions , Ġthe Ġdatasets Ġand Ġclassification Ġtax onom ies Ġtend Ġto Ġbe Ġsmall , Ġand Ġrule - based Ġmethods Ġoften Ġachieve Ġstrong Ġresults ĠB IB REF 45 Ġ. ĠRoberts Ġet Ġal . ĠB IB REF 3 Ġcreated Ġthe Ġlargest Ġbiomedical Ġquestion Ġclassification Ġdataset Ġto Ġdate , Ġannot ating Ġ2 , 9 37 Ġconsumer Ġhealth Ġquestions Ġdrawn Ġfrom Ġthe ĠGenetic Ġand ĠRare ĠDiseases Ġ( G ARD ) Ġquestion Ġdatabase Ġwith Ġ13 Ġquestion Ġtypes , Ġsuch Ġas Ġanatomy , Ġdisease Ġcause , Ġdiagnosis , Ġdisease Ġmanagement , Ġand Ġprog n oses . ĠRoberts Ġet Ġal . ĠB IB REF 3 Ġfound Ġthese Ġquestions Ġlargely Ġresistant Ġto Ġlearning - based Ġmethods Ġdeveloped Ġfor ĠT REC Ġquestions . ĠTheir Ġbest Ġmodel Ġ( CP T 2 ), Ġshown Ġin ĠTable ĠT AB REF 17 Ġ, Ġmakes Ġuse Ġof Ġstemming Ġand Ġlists Ġof Ġsem antically Ġrelated Ġwords Ġand Ġcue Ġphrases Ġto Ġachieve Ġ80 . 4 % Ġaccuracy . ĠB ERT - Q C Ġreaches Ġ84 . 9 % Ġaccuracy Ġon Ġthis Ġdataset , Ġan Ġincrease Ġof Ġ+ 4 . 5 % Ġover Ġthe Ġbest Ġprevious Ġmodel . ĠWe Ġalso Ġcompare Ġperformance Ġon Ġthe Ġrecently Ġreleased ĠMLB io Med L AT Ġdataset ĠB IB REF 4 Ġ, Ġa Ġmulti - label Ġbiomedical Ġquestion Ġclassification Ġdataset Ġwith Ġ780 Ġquestions Ġlabeled Ġusing Ġ88 Ġclassification Ġtypes Ġdrawn Ġfrom Ġ133 ĠUnified ĠMedical ĠLanguage ĠSystem Ġ( U ML S ) Ġcategories . ĠTable ĠT AB REF 18 Ġshows ĠB ERT - Q C Ġexceeds Ġtheir Ġbest Ġmodel , Ġfocus - driven Ġsemantic Ġfeatures Ġ( F DS F ), Ġby Ġ+ 0 . 05 ĠMicro - F 1 Ġand Ġ+ 3 % Ġaccuracy . </s> Error ĠAnalysis </s> We Ġperformed Ġan Ġerror Ġanalysis Ġon Ġ50 ĠARC Ġquestions Ġwhere Ġthe ĠB ERT - Q C Ġsystem Ġdid Ġnot Ġpredict Ġthe Ġcorrect Ġlabel , Ġwith Ġa Ġsummary Ġof Ġmajor Ġerror Ġcategories Ġlisted Ġin ĠTable ĠT AB REF 20 Ġ. </s> Ass oci ative ĠErrors : ĠIn Ġ35 % Ġof Ġcases , Ġpredicted Ġlabels Ġwere Ġnearly Ġcorrect , Ġdiffering Ġfrom Ġthe Ġcorrect Ġlabel Ġonly Ġby Ġthe Ġfinest - gr ained Ġ( leaf ) Ġelement Ġof Ġthe Ġhierarchical Ġlabel Ġ( for Ġexample , Ġpredicting ĠMatter ĠIN LINE FORM 0 ĠChanges Ġof ĠState ĠIN LINE FORM 1 ĠBo iling Ġinstead Ġof ĠMatter ĠIN LINE FORM 2 ĠChanges Ġof ĠState ĠIN LINE FORM 3 ĠFree zing ). ĠThe Ġbulk Ġof Ġthe Ġremaining Ġerrors Ġwere Ġdue Ġto Ġquestions Ġcontaining Ġhighly Ġcorrelated Ġwords Ġwith Ġa Ġdifferent Ġclass , Ġor Ġclasses Ġthemselves Ġbeing Ġhighly Ġcorrelated . ĠFor Ġexample , Ġa Ġspecific Ġquestion Ġabout ĠWeather ĠModels Ġdiscusses ĠâĢ ľ en vironments âĢ Ŀ Ġchanging Ġover ĠâĢ ľ mill ions Ġof Ġyears âĢ Ŀ , Ġwhere Ġdiscussions Ġof Ġenvironments Ġand Ġlong Ġtime Ġperiods Ġtend Ġto Ġbe Ġassociated Ġwith Ġquestions Ġabout ĠLocations Ġof ĠFoss ils . ĠSimilarly , Ġa Ġquestion Ġcontaining Ġthe Ġword ĠâĢ ľ ev ap oration âĢ Ŀ Ġcould Ġbe Ġprimarily Ġfocused Ġon Ġeither ĠChanges Ġof ĠState Ġor Ġthe ĠWater ĠCycle Ġ( cloud Ġgeneration ), Ġand Ġmust Ġrely Ġon Ġknowledge Ġfrom Ġthe Ġentire Ġquestion Ġtext Ġto Ġdetermine Ġthe Ġcorrect Ġproblem Ġdomain . ĠWe Ġbelieve Ġthese Ġassoci ative Ġerrors Ġare Ġaddress able Ġtechnical Ġchallenges Ġthat Ġcould Ġultimately Ġlead Ġto Ġincreased Ġperformance Ġin Ġsubsequent Ġmodels . </s> Er rors Ġspecific Ġto Ġthe Ġmultiple - choice Ġdomain : ĠWe Ġobserved Ġthat Ġusing Ġboth Ġquestion Ġand Ġall Ġmultiple Ġchoice Ġanswer Ġtext Ġproduced Ġlarge Ġgains Ġin Ġquestion Ġclassification Ġperformance ĠâĢĵ Ġfor Ġexample , ĠB ERT - Q C Ġperformance Ġincreases Ġfrom Ġ0 . 516 Ġ( question Ġonly ) Ġto Ġ0 . 654 Ġ( question Ġand Ġall Ġfour Ġanswer Ġcandidates ), Ġan Ġincrease Ġof Ġ0 . 138 ĠMAP . ĠOur Ġerror Ġanalysis Ġobserved Ġthat Ġwhile Ġthis Ġsubstantially Ġincreases ĠQC Ġperformance , Ġit Ġchanges Ġthe Ġdistribution Ġof Ġerrors Ġmade Ġby Ġthe Ġsystem . ĠSpecifically , Ġ25 % Ġof Ġerrors Ġbecome Ġhighly Ġcorrelated Ġwith Ġan Ġincorrect Ġanswer Ġcandidate , Ġwhich Ġ( we Ġshow Ġin ĠSection ĠSEC REF 5 Ġ) Ġcan Ġreduce Ġthe Ġperformance Ġof ĠQ A Ġsol vers . </s> Question ĠAn sw ering Ġwith ĠQC ĠLab els </s> Because Ġof Ġthe Ġchallenges Ġof Ġerror ful Ġlabel Ġpredictions Ġcorrel ating Ġwith Ġincorrect Ġanswers , Ġit Ġis Ġdifficult Ġto Ġdetermine Ġthe Ġultimate Ġbenefit Ġa ĠQ A Ġmodel Ġmight Ġreceive Ġfrom Ġreporting ĠQC Ġperformance Ġin Ġisolation . ĠCou pling ĠQ A Ġand ĠQC Ġsystems Ġcan Ġoften Ġbe Ġlabor ious ĠâĢĵ Ġeither Ġa Ġlarge Ġnumber Ġof Ġindependent Ġsol vers Ġtargeted Ġto Ġspecific Ġquestion Ġtypes Ġmust Ġbe Ġconstructed ĠB IB REF 46 Ġ, Ġor Ġan Ġexisting Ġsingle Ġmodel Ġmust Ġbe Ġable Ġto Ġproduct ively Ġincorporate Ġquestion Ġclassification Ġinformation . ĠHere Ġwe Ġdem ost rate Ġthe Ġlatter ĠâĢĵ Ġthat Ġa ĠB ERT ĠQ A Ġmodel Ġis Ġable Ġto Ġincorporate Ġquestion Ġclassification Ġinformation Ġthrough Ġquery Ġexpansion . ĠB ERT ĠB IB REF 43 Ġrecently Ġdemonstrated Ġstate - of - the - art Ġperformance Ġon Ġbenchmark Ġquestion Ġanswering Ġdatasets Ġsuch Ġas ĠSQU a D ĠB IB REF 47 Ġ, Ġand Ġnear Ġhuman - level Ġperformance Ġon ĠSW AG ĠB IB REF 48 Ġ. ĠSimilarly , ĠPan Ġet Ġal . ĠB IB REF 49 Ġdemonstrated Ġthat ĠB ERT Ġachieves Ġthe Ġhighest Ġaccuracy Ġon Ġthe Ġmost Ġchallenging Ġsubset Ġof ĠARC Ġscience Ġquestions . ĠWe Ġmake Ġuse Ġof Ġa ĠB ERT ĠQ A Ġmodel Ġusing Ġthe Ġsame ĠQ A Ġparadigm Ġdescribed Ġby ĠPan Ġet Ġal . ĠB IB REF 49 Ġ, Ġwhere ĠQ A Ġis Ġmodeled Ġas Ġa Ġnext - sent ence Ġprediction Ġtask Ġthat Ġpredicts Ġthe Ġlikelihood Ġof Ġa Ġgiven Ġmultiple Ġchoice Ġanswer Ġcandidate Ġfollowing Ġthe Ġquestion Ġtext . ĠWe Ġevaluate Ġthe Ġquestion Ġtext Ġand Ġthe Ġtext Ġof Ġeach Ġmultiple Ġchoice Ġanswer Ġcandidate Ġseparately , Ġwhere Ġthe Ġanswer Ġcandidate Ġwith Ġthe Ġhighest Ġprob abl ity Ġis Ġselected Ġas Ġthe Ġpredicted Ġanswer Ġfor Ġa Ġgiven Ġquestion . ĠPerformance Ġis Ġevaluated Ġusing ĠPrecision @ 1 ĠB IB REF 36 Ġ. ĠAdditional Ġmodel Ġdetails Ġand Ġhyper param eters Ġare Ġincluded Ġin Ġthe ĠAppendix . </s> We Ġincorporate ĠQC Ġinformation Ġinto Ġthe ĠQ A Ġprocess Ġby Ġimplementing Ġa Ġvariant Ġof Ġa Ġquery Ġexpansion Ġmodel ĠB IB REF 50 Ġ. ĠSpecifically , Ġfor Ġa Ġgiven Ġ{ question , ĠQC _ label } Ġpair , Ġwe Ġexpand Ġthe Ġquestion Ġtext Ġby Ġconc aten ating Ġthe Ġdefinition Ġtext Ġof Ġthe Ġquestion Ġclassification Ġlabel Ġto Ġthe Ġstart Ġof Ġthe Ġquestion . ĠWe Ġuse Ġof Ġthe Ġtop Ġpredicted Ġquestion Ġclassification Ġlabel Ġfor Ġeach Ġquestion . ĠBecause ĠQC Ġlabels Ġare Ġhierarchical , Ġwe Ġappend Ġthe Ġlabel Ġdefinition Ġtext Ġfor Ġeach Ġlevel Ġof Ġthe Ġlabel ĠIN LINE FORM 0 Ġ. ĠAn Ġexam pe Ġof Ġthis Ġprocess Ġis Ġshown Ġin ĠTable ĠT AB REF 23 Ġ. </s> Figure ĠFIG REF 24 Ġshows ĠQ A Ġpe formance Ġusing Ġpredicted Ġlabels Ġfrom Ġthe ĠB ERT - Q C Ġmodel , Ġcompared Ġto Ġa Ġbaseline Ġmodel Ġthat Ġdoes Ġnot Ġcontain Ġquestion Ġclassification Ġinformation . ĠAs Ġpredicted Ġby Ġthe Ġerror Ġanalysis , Ġwhile Ġa Ġmodel Ġtrained Ġwith Ġquestion Ġand Ġanswer Ġcandidate Ġtext Ġperforms Ġbetter Ġat ĠQC Ġthan Ġa Ġmodel Ġusing Ġquestion Ġtext Ġalone , Ġa Ġlarge Ġproportion Ġof Ġthe Ġincorrect Ġpredictions Ġbecome Ġassociated Ġwith Ġa Ġnegative Ġanswer Ġcandidate , Ġreducing Ġoverall ĠQ A Ġperformance , Ġand Ġhighlighting Ġthe Ġimportance Ġof Ġevaluating ĠQC Ġand ĠQ A Ġmodels Ġtogether . ĠWhen Ġusing ĠB ERT - Q C Ġtrained Ġon Ġquestion Ġtext Ġalone , Ġat Ġthe Ġfinest Ġlevel Ġof Ġspecificity Ġ( L 6 ) Ġwhere Ġoverall Ġquestion Ġclassification Ġaccuracy Ġis Ġ57 . 8 % ĠP @ 1 , Ġquestion Ġclassification Ġsignificantly Ġimproves ĠQ A Ġperformance Ġby Ġ+ 1 . 7 % ĠP @ 1 ĠIN LINE FORM 0 Ġ. ĠUsing Ġgold Ġlabels Ġshows Ġceiling ĠQ A Ġperformance Ġcan Ġreach Ġ+ 10 . 0 % ĠP @ 1 Ġover Ġbaseline , Ġdemonstrating Ġthat Ġas Ġquestion Ġclassification Ġmodel Ġperformance Ġimproves , Ġsubstantial Ġfuture Ġgains Ġare Ġpossible . ĠAn Ġanalysis Ġof Ġexpected Ġgains Ġfor Ġa Ġgiven Ġlevel Ġof ĠQC Ġperformance Ġis Ġincluded Ġin Ġthe ĠAppendix , Ġshowing Ġapproximately Ġlinear Ġgains Ġin ĠQ A Ġperformance Ġabove Ġbaseline Ġfor ĠQC Ġsystems Ġable Ġto Ġachieve Ġover Ġ40 % Ġclassification Ġaccuracy . ĠBelow Ġthis Ġlevel , Ġthe Ġdecreased Ġperformance Ġfrom Ġnoise Ġinduced Ġby Ġincorrect Ġlabels Ġsurpass es Ġgains Ġfrom Ġcorrect Ġlabels . </s> Hyper param eters : ĠPilot Ġexperiments Ġon Ġboth Ġpre - trained ĠB ERT - Base Ġand ĠB ERT - Large Ġcheckpoints Ġshowed Ġsimilar Ġperformance Ġbenefits Ġat Ġthe Ġfinest Ġlevels Ġof Ġquestion Ġclassification Ġgran ularity Ġ( L 6 ), Ġbut Ġthe ĠB ERT - Large Ġmodel Ġdemonstrated Ġhigher Ġoverall Ġbaseline Ġperformance , Ġand Ġlarger Ġincremental Ġbenefits Ġat Ġlower Ġlevels Ġof ĠQC Ġgran ularity , Ġso Ġwe Ġevaluated Ġusing Ġthat Ġmodel . ĠWe Ġlightly Ġtuned Ġhyper param eters Ġon Ġthe Ġdevelopment Ġset Ġsurrounding Ġthose Ġreported Ġby ĠDev lin Ġet Ġal . ĠB IB REF 43 Ġ, Ġand Ġultimately Ġsettled Ġon Ġparameters Ġsimilar Ġto Ġtheir Ġoriginal Ġwork , Ġtempered Ġby Ġtechnical Ġlimitations Ġin Ġrunning Ġthe ĠB ERT - Large Ġmodel Ġon Ġavailable Ġhardware : Ġmaximum Ġsequence Ġlength Ġ= Ġ128 , Ġbatch Ġsize Ġ= Ġ16 , Ġlearning Ġrate : Ġ1 e - 5 . ĠWe Ġreport Ġperformance Ġas Ġthe Ġaverage Ġof Ġ10 Ġruns Ġfor Ġeach Ġdat ap oint . ĠThe Ġnumber Ġof Ġepoch s Ġwere Ġtuned Ġon Ġeach Ġrun Ġon Ġthe Ġdevelopment Ġset Ġ( to Ġa Ġmaximum Ġof Ġ8 Ġepoch s ), Ġwhere Ġmost Ġmodels Ġconver ged Ġto Ġmaximum Ġperformance Ġwithin Ġ5 Ġepoch s . </s> Pre ference Ġfor Ġunc or related Ġerrors Ġin Ġmultiple Ġchoice Ġquestion Ġclassification : ĠWe Ġprimarily Ġreport ĠQ A Ġperformance Ġusing ĠB ERT - Q C Ġtrained Ġusing Ġtext Ġfrom Ġonly Ġthe Ġmultiple Ġchoice Ġquestions Ġand Ġnot Ġtheir Ġanswer Ġcandidates . ĠWhile Ġthis Ġmodel Ġachieved Ġlower Ġoverall ĠQC Ġperformance Ġcompared Ġto Ġthe Ġmodel Ġtrained Ġwith Ġboth Ġquestion Ġand Ġmultiple Ġchoice Ġanswer Ġcandidate Ġtext , Ġit Ġachieved Ġslightly Ġhigher Ġperformance Ġin Ġthe ĠQ A + Q C Ġsetting . ĠOur Ġerror Ġanalysis Ġin ĠSection ĠSEC REF 21 Ġshows Ġthat Ġthough Ġmodels Ġtrained Ġon Ġboth Ġquestion Ġand Ġanswer Ġtext Ġcan Ġachieve Ġhigher ĠQC Ġperformance , Ġwhen Ġthey Ġmake ĠQC Ġerrors , Ġthe Ġerrors Ġtend Ġto Ġbe Ġhighly Ġcorrelated Ġwith Ġan Ġincorrect Ġanswer Ġcandidate , Ġwhich Ġcan Ġsubstantially Ġreduce ĠQ A Ġperformance . ĠThis Ġis Ġan Ġimportant Ġresult Ġfor Ġquestion Ġclassification Ġin Ġthe Ġcontext Ġof Ġmultiple Ġchoice Ġexams . In Ġthe Ġcontext Ġof Ġmultiple Ġchoice Ġexams , Ġcorrelated Ġnoise Ġcan Ġsubstantially Ġreduce ĠQ A Ġperformance , Ġmeaning Ġthe Ġkinds Ġof Ġerrors Ġthat Ġa Ġmodel Ġmakes Ġare Ġimportant , Ġand Ġevaluating ĠQC Ġperformance Ġin Ġcontext Ġwith ĠQ A Ġmodels Ġthat Ġmake Ġuse Ġof Ġthose ĠQC Ġsystems Ġis Ġcritical . </s> Related Ġto Ġthis Ġresult , Ġwe Ġprovide Ġan Ġanalysis Ġof Ġthe Ġnoise Ġsensitivity Ġof Ġthe ĠQ A + Q C Ġmodel Ġfor Ġdifferent Ġlevels Ġof Ġquestion Ġclassification Ġprediction Ġaccuracy . ĠHere , Ġwe Ġpert urb Ġgold Ġquestion Ġlabels Ġby Ġrandomly Ġselecting Ġa Ġproportion Ġof Ġquestions Ġ( between Ġ5 % Ġand Ġ40 %) Ġand Ġrandomly Ġassigning Ġthat Ġquestion Ġa Ġdifferent Ġlabel . ĠFigure ĠFIG REF 36 Ġshows Ġthat Ġthis Ġunc or related Ġnoise Ġprovides Ġroughly Ġlinear Ġdecreases Ġin Ġperformance , Ġand Ġstill Ġshows Ġmoderate Ġgains Ġat Ġ60 % Ġaccuracy Ġ( 40 % Ġnoise ) Ġwith Ġunc or related Ġnoise . ĠThis Ġsuggests Ġthat Ġwhen Ġmaking Ġerrors , Ġmaking Ġrandom Ġerrors Ġ( that Ġare Ġnot Ġcorrelated Ġto Ġincorrect Ġmultiple Ġchoice Ġanswers ) Ġis Ġpreferential . </s> Training Ġwith Ġpredicted Ġlabels : ĠWe Ġobserved Ġsmall Ġgains Ġwhen Ġtraining Ġthe ĠB ERT - Q A Ġmodel Ġwith Ġpredicted ĠQC Ġlabels . ĠWe Ġgenerate Ġpredicted Ġlabels Ġfor Ġthe Ġtraining Ġset Ġusing Ġ5 - fold Ġcross valid ation Ġover Ġonly Ġthe Ġtraining Ġquestions . </s> Statistics : ĠWe Ġuse Ġnon - param etric Ġboot strap Ġres am pling Ġto Ġcompare Ġbaseline Ġ( no Ġlabel ) Ġand Ġexperimental Ġ( Q C Ġlabeled ) Ġruns Ġof Ġthe ĠQ A + Q C Ġexperiment . ĠBecause Ġthe ĠB ERT - Q A Ġmodel Ġproduces Ġdifferent Ġperformance Ġvalues Ġacross Ġsuccessive Ġruns , Ġwe Ġperform Ġ10 Ġruns Ġof Ġeach Ġcondition . ĠWe Ġthen Ġcompute Ġpair wise Ġp - values Ġfor Ġeach Ġof Ġthe Ġ10 Ġno Ġlabel Ġand ĠQC Ġlabeled Ġruns Ġ( gener ating Ġ100 Ġcomparisons ), Ġthen Ġuse ĠFisher 's Ġmethod Ġto Ġcombine Ġthese Ġinto Ġa Ġfinal Ġstatistic . </s> Question Ġclassification Ġpaired Ġwith Ġquestion Ġanswering Ġshows Ġstatistically Ġsignificant Ġgains Ġof Ġ+ 1 . 7 % ĠP @ 1 Ġat ĠL 6 Ġusing Ġpredicted Ġlabels , Ġand Ġa Ġceiling Ġgain Ġof Ġup Ġto Ġ+ 10 % ĠP @ 1 Ġusing Ġgold Ġlabels . ĠThe ĠQ A Ġperformance Ġgraph Ġin ĠFigure ĠFIG REF 24 Ġcontains Ġtwo Ġdeviations Ġfrom Ġthe Ġexpectation Ġof Ġlinear Ġgains Ġwith Ġincreasing Ġspecificity , Ġat ĠL 1 Ġand ĠL 3 . ĠRegion Ġat ĠIN LINE FORM 0 ĠOn Ġgold Ġlabels , ĠL 3 Ġprovides Ġsmall Ġgains Ġover ĠL 2 , Ġwhere Ġas ĠL 4 Ġprovides Ġlarge Ġgains Ġover ĠL 3 . ĠWe Ġhypothes ize Ġthat Ġthis Ġis Ġbecause Ġapproximately Ġ57 % Ġof Ġquestion Ġlabels Ġbelong Ġto Ġthe ĠEarth ĠScience Ġor ĠLife ĠScience Ġcategories Ġwhich Ġhave Ġmuch Ġmore Ġdepth Ġthan Ġbreadth Ġin Ġthe Ġstandardized Ġscience Ġcurriculum , Ġand Ġas Ġsuch Ġthese Ġcategories Ġare Ġprimarily Ġdifferentiated Ġfrom Ġbroad Ġtopics Ġinto Ġdetailed Ġproblem Ġtypes Ġat Ġlevels ĠL 4 Ġthrough ĠL 6 . ĠMost Ġother Ġcurriculum Ġcategories Ġhave Ġmore Ġbreadth Ġthan Ġdepth , Ġand Ġshow Ġstrong Ġ( but Ġnot Ġnecessarily Ġfull ) Ġdifferentiation Ġat ĠL 2 . ĠRegion Ġat ĠIN LINE FORM 1 ĠPred icted Ġperformance Ġat ĠL 1 Ġis Ġhigher Ġthan Ġgold Ġperformance Ġat ĠL 1 . ĠWe Ġhypothes ize Ġthis Ġis Ġbecause Ġwe Ġtrain Ġusing Ġpredicted Ġrather Ġthan Ġgold Ġlabels , Ġwhich Ġprovides Ġa Ġboost Ġin Ġperformance . ĠTraining Ġon Ġgold Ġlabels Ġand Ġtesting Ġon Ġpredicted Ġlabels Ġsubstantially Ġreduces Ġthe Ġdifference Ġbetween Ġgold Ġand Ġpredicted Ġperformance . </s> Though Ġinitial Ġraw Ġinter annot ator Ġagreement Ġwas Ġmeasured Ġat ĠIN LINE FORM 0 Ġ, Ġto Ġmaximize Ġthe Ġquality Ġof Ġthe Ġannotation Ġthe Ġannot ators Ġperformed Ġa Ġsecond Ġpass Ġwhere Ġall Ġdisagreements Ġwere Ġmanually Ġresolved . ĠTable ĠT AB REF 30 Ġshows Ġquestion Ġclassification Ġperformance Ġof Ġthe ĠB ERT - Q C Ġmodel Ġat Ġ57 . 8 % ĠP @ 1 , Ġmeaning Ġ42 . 2 % Ġof Ġthe Ġpredicted Ġlabels Ġwere Ġdifferent Ġthan Ġthe Ġgold Ġlabels . ĠThe Ġquestion Ġclassification Ġerror Ġanalysis Ġin ĠTable ĠT AB REF 20 Ġfound Ġthat Ġof Ġthese Ġ42 . 2 % Ġof Ġerror ful Ġpredictions , Ġ10 % Ġof Ġerrors Ġ( 4 . 2 % Ġof Ġtotal Ġlabels ) Ġwere Ġcaused Ġby Ġthe Ġgold Ġlabels Ġbeing Ġincorrect . ĠThis Ġallows Ġus Ġto Ġestimate Ġthat Ġthe Ġoverall Ġquality Ġof Ġthe Ġannotation Ġ( the Ġproportion Ġof Ġquestions Ġthat Ġhave Ġa Ġcorrect Ġhuman Ġauthored Ġlabel ) Ġis Ġapproximately Ġ96 %. </s> Autom ating ĠError ĠAn alyses Ġwith ĠQC </s> Detailed Ġerror Ġanalyses Ġfor Ġquestion Ġanswering Ġsystems Ġare Ġtypically Ġlabor Ġintensive , Ġoften Ġrequiring Ġhours Ġor Ġdays Ġto Ġperform Ġmanually . ĠAs Ġa Ġresult Ġerror Ġanalyses Ġare Ġtypically Ġcompleted Ġinf requently , Ġin Ġspite Ġof Ġtheir Ġutility Ġto Ġkey Ġdecisions Ġin Ġthe Ġal g ort ith m Ġor Ġknowledge Ġconstruction Ġprocess . ĠHere Ġwe Ġshow Ġhaving Ġaccess Ġto Ġdetailed Ġquestion Ġclassification Ġlabels Ġspecifying Ġfine - gr ained Ġproblem Ġdomains Ġprovides Ġa Ġmechanism Ġto Ġautomatically Ġgenerate Ġerror Ġanalyses Ġin Ġseconds Ġinstead Ġof Ġdays . </s> To Ġillustrate Ġthe Ġutility Ġof Ġthis Ġapproach , ĠTable ĠT AB REF 26 Ġshows Ġthe Ġperformance Ġof Ġthe ĠB ERT ĠQ A + Q C Ġmodel Ġbroken Ġdown Ġby Ġspecific Ġquestion Ġclasses . ĠThis Ġallows Ġautomatically Ġidentifying Ġa Ġgiven Ġmodel 's Ġstrengths ĠâĢĵ Ġfor Ġexample , Ġhere Ġquestions Ġabout ĠHuman ĠHealth , ĠMaterial ĠProperties , Ġand ĠEarth 's ĠInner ĠCore Ġare Ġwell Ġaddressed Ġby Ġthe ĠB ERT - Q A Ġmodel , Ġand Ġachieve Ġwell Ġabove Ġthe Ġaverage ĠQ A Ġperformance Ġof Ġ49 %. ĠSimilarly , Ġareas Ġof Ġdeficit Ġinclude ĠChanges Ġof ĠState , ĠReprodu ction , Ġand ĠFood ĠChain ĠProcess es Ġquestions , Ġwhich Ġsee Ġbelow - average ĠQ A Ġperformance . ĠThe Ġlowest Ġperforming Ġclass , ĠSafety ĠProcedures , Ġdemonstrates Ġthat Ġwhile Ġthis Ġmodel Ġhas Ġstrong Ġperformance Ġin Ġmany Ġareas Ġof Ġscientific Ġreasoning , Ġit Ġis Ġworse Ġthan Ġchance Ġat Ġanswering Ġquestions Ġabout Ġsafety , Ġand Ġwould Ġbe Ġinappropriate Ġto Ġdeploy Ġfor Ġsafety - critical Ġtasks . </s> While Ġthis Ġanalysis Ġis Ġshown Ġat Ġan Ġintermediate Ġ( L 2 ) Ġlevel Ġof Ġspecificity Ġfor Ġspace , Ġmore Ġdetailed Ġanalyses Ġare Ġpossible . ĠFor Ġexample , Ġoverall ĠQ A Ġperformance Ġon ĠScientific ĠIn ference Ġquestions Ġis Ġnear Ġaverage Ġ( 47 %), Ġbut Ġincreasing Ġgran ularity Ġto ĠL 3 Ġwe Ġobserve Ġthat Ġquestions Ġaddressing ĠExperiment ĠDesign Ġor ĠMaking ĠInf erences ĠâĢĵ Ġchallenging Ġquestions Ġeven Ġfor Ġhumans ĠâĢĵ Ġperform Ġpoorly Ġ( 33 % Ġand Ġ20 %) Ġwhen Ġanswered Ġby Ġthe ĠQ A Ġsystem . ĠThis Ġallows Ġa Ġsystem Ġdesigner Ġto Ġintellig ently Ġtarget Ġproblem - specific Ġknowledge Ġresources Ġand Ġinference Ġmethods Ġto Ġaddress Ġdeficits Ġin Ġspecific Ġareas . </s> Conclusion </s> Question Ġclassification Ġcan Ġenable Ġtarget ting Ġquestion Ġanswering Ġmodels , Ġbut Ġis Ġchallenging Ġto Ġimplement Ġwith Ġhigh Ġperformance Ġwithout Ġusing Ġrule - based Ġmethods . ĠIn Ġthis Ġwork Ġwe Ġgenerate Ġthe Ġmost Ġfine - gr ained Ġchallenge Ġdataset Ġfor Ġquestion Ġclassification , Ġusing Ġcomplex Ġand Ġsynt act ically Ġdiverse Ġquestions , Ġand Ġshow Ġgains Ġof Ġup Ġto Ġ12 % Ġare Ġpossible Ġwith Ġour Ġquestion Ġclassification Ġmodel Ġacross Ġdatasets Ġin Ġopen , Ġscience , Ġand Ġmedical Ġdomains . ĠThis Ġmodel Ġis Ġthe Ġfirst Ġdemonstration Ġof Ġa Ġquestion Ġclassification Ġmodel Ġachieving Ġstate - of - the - art Ġresults Ġacross Ġbenchmark Ġdatasets Ġin Ġopen , Ġscience , Ġand Ġmedical Ġdomains . ĠWe Ġfurther Ġdemonstrate Ġattending Ġto Ġquestion Ġtype Ġcan Ġsignificantly Ġimprove Ġquestion Ġanswering Ġperformance , Ġwith Ġlarge Ġgains Ġpossible Ġas Ġqu es ion Ġclassification Ġperformance Ġimproves . ĠOur Ġerror Ġanalysis Ġsuggests Ġthat Ġdeveloping Ġhigh - pre cision Ġmethods Ġof Ġquestion Ġclassification Ġindependent Ġof Ġtheir Ġrecall Ġcan Ġoffer Ġthe Ġopportunity Ġto Ġincrement ally Ġmake Ġuse Ġof Ġthe Ġbenefits Ġof Ġquestion Ġclassification Ġwithout Ġsuffering Ġthe Ġconsequences Ġof Ġclassification Ġerrors Ġon ĠQ A Ġperformance . </s> Resources </s> Our ĠAppendix Ġand Ġsupplementary Ġmaterial Ġ( available Ġat Ġhttp :// www . c ognitive ai . org / ex plan ation bank /) Ġincludes Ġdata , Ġcode , Ġexperiment Ġdetails , Ġand Ġnegative Ġresults . </s> Acknowled gements </s> The Ġauthors Ġwish Ġto Ġthank ĠElizabeth ĠW ain wright Ġand ĠStephen ĠMar mor stein Ġfor Ġpilot ing Ġan Ġearlier Ġversion Ġof Ġthe Ġquestion Ġclassification Ġannotation . ĠWe Ġthank Ġthe ĠAllen ĠIns itute Ġfor ĠArtificial ĠIntelligence Ġand ĠNational ĠScience ĠF oun ation Ġ( NS F Ġ18 159 48 Ġto ĠPJ ) Ġfor Ġfunding Ġthis Ġwork . </s> An notation </s> Class ification ĠTax onomy : ĠThe Ġfull Ġclassification Ġtax onomy Ġis Ġincluded Ġin Ġseparate Ġfiles , Ġboth Ġcoupled Ġwith Ġdefinitions , Ġand Ġas Ġa Ġgraphical Ġvisualization . </s> An notation ĠProcedure : ĠPrimary Ġannotation Ġtook Ġplace Ġover Ġapproximately Ġ8 Ġweeks . ĠAn not ators Ġwere Ġinstructed Ġto Ġprovide Ġup Ġto Ġ2 Ġlabels Ġfrom Ġthe Ġfull Ġclassification Ġtax onomy Ġ( 462 Ġlabels ) Ġthat Ġwere Ġappropriate Ġfor Ġeach Ġquestion , Ġand Ġto Ġprovide Ġthe Ġmost Ġspecific Ġlabel Ġavailable Ġin Ġthe Ġtax onomy Ġfor Ġa Ġgiven Ġquestion . ĠOf Ġthe Ġ4 62 Ġlabels Ġin Ġthe Ġclassification Ġtax onomy , Ġthe ĠARC Ġquestions Ġhad Ġnon - zero Ġcounts Ġin Ġ406 Ġquestion Ġtypes . ĠRare ly , Ġquestions Ġwere Ġencountered Ġby Ġannot ators Ġthat Ġdid Ġnot Ġclearly Ġfit Ġinto Ġa Ġlabel Ġat Ġthe Ġend Ġof Ġthe Ġtax onomy , Ġand Ġin Ġthese Ġcases Ġthe Ġannot ators Ġwere Ġinstructed Ġto Ġchoose Ġa Ġmore Ġgeneric Ġlabel Ġhigher Ġup Ġthe Ġtax onomy Ġthat Ġwas Ġappropriate . ĠThis Ġoccurred Ġwhen Ġthe Ġproduction Ġtax onomy Ġfailed Ġto Ġhave Ġspecific Ġcategories Ġfor Ġinf requent Ġquestions Ġtesting Ġknowledge Ġthat Ġis Ġnot Ġa Ġstandard Ġpart Ġof Ġthe Ġscience Ġcurriculum . ĠFor Ġexample , Ġthe Ġquestion : </s> Which Ġmaterial Ġis Ġthe Ġbest Ġnatural Ġresource Ġto Ġuse Ġfor Ġmaking Ġwater - resistant Ġshoes ? Ġ( A ) Ġcotton Ġ( B ) Ġleather Ġ( C ) Ġplastic Ġ( D ) Ġwool </s> tests Ġa Ġstudent 's Ġknowledge Ġof Ġthe Ġwater Ġresistance Ġof Ġdifferent Ġmaterials . ĠBecause Ġthis Ġis Ġnot Ġa Ġstandard Ġpart Ġof Ġthe Ġcurriculum , Ġand Ġwasn 't Ġidentified Ġas Ġa Ġcommon Ġtopic Ġin Ġthe Ġtraining Ġquestions , Ġthe Ġannot ators Ġtag Ġthis Ġquestion Ġas Ġbelonging Ġto ĠMatter ĠIN LINE FORM 0 ĠProperties Ġof ĠMaterials , Ġrather Ġthan Ġa Ġmore Ġspecific Ġcategory . </s> Questions Ġfrom Ġthe Ġtraining , Ġdevelopment , Ġand Ġtest Ġsets Ġwere Ġrandomly Ġshuff led Ġto Ġcounter balance Ġany Ġlearning Ġeffects Ġduring Ġthe Ġannotation Ġprocedure , Ġbut Ġwere Ġpresented Ġin Ġgrade Ġorder Ġ( 3 rd Ġto Ġ9 th Ġgrade ) Ġto Ġreduce Ġcontext Ġswitching Ġ( a Ġgiven Ġgrade Ġlevel Ġtends Ġto Ġuse Ġa Ġsimilar Ġsubset Ġof Ġthe Ġtax onomy ĠâĢĵ Ġfor Ġexample , Ġ3 rd Ġgrade Ġquestions Ġgenerally Ġdo Ġnot Ġaddress ĠChemical ĠEqu ations Ġor ĠNew tons Ġ1 st ĠLaw Ġof ĠMotion ). </s> Inter annot ator ĠAgreement : ĠTo Ġincrease Ġquality Ġand Ġconsistency , Ġeach Ġannot ator Ġannot ated Ġthe Ġentire Ġdataset Ġof Ġ7 , 787 Ġquestions . ĠTwo Ġannot ators Ġwere Ġused , Ġwith Ġthe Ġlead Ġannot ator Ġpossessing Ġprevious Ġprofessional Ġdomain Ġexpertise . ĠAn notation Ġproceeded Ġin Ġa Ġtwo - stage Ġprocess , Ġwhere Ġin Ġstage Ġ1 Ġannot ators Ġcompleted Ġtheir Ġannotation Ġindependently , Ġand Ġin Ġstage Ġ2 Ġeach Ġof Ġthe Ġquestions Ġwhere Ġthe Ġannot ators Ġdid Ġnot Ġhave Ġcomplete Ġagreement Ġwere Ġmanually Ġresolved Ġby Ġthe Ġannot ators , Ġresulting Ġin Ġhigh - quality Ġclassification Ġannotation . </s> Because Ġeach Ġquestion Ġcan Ġhave Ġup Ġto Ġtwo Ġlabels , Ġwe Ġtreat Ġeach Ġlabel Ġfor Ġa Ġgiven Ġquestion Ġas Ġa Ġseparate Ġevaluation Ġof Ġinter annot ator Ġagreement . ĠThat Ġis , Ġfor Ġquestions Ġwhere Ġboth Ġannot ators Ġlabeled Ġeach Ġquestion Ġas Ġhaving Ġ1 Ġor Ġ2 Ġlabels , Ġwe Ġtreat Ġthis Ġas Ġ1 Ġor Ġ2 Ġseparate Ġevaluations Ġof Ġinter annot ator Ġagreement . ĠFor Ġcases Ġwhere Ġone Ġannot ator Ġlabeled Ġas Ġquestion Ġas Ġhaving Ġ1 Ġlabel , Ġand Ġthe Ġother Ġannot ator Ġlabeled Ġthat Ġsame Ġquestion Ġas Ġhaving Ġ2 Ġlabels , Ġwe Ġconservative ly Ġtreat Ġthis Ġas Ġtwo Ġseparate Ġinter annot ator Ġagreements Ġwhere Ġone Ġannot ator Ġfailed Ġto Ġspecify Ġthe Ġsecond Ġlabel Ġand Ġhad Ġzero Ġagreement Ġon Ġthat Ġunspecified Ġlabel . </s> Though Ġthe Ġclassification Ġprocedure Ġwas Ġfine - gr ained Ġcompared Ġto Ġother Ġquestion Ġclassification Ġtax onom ies , Ġcontaining Ġan Ġunusually Ġlarge Ġnumber Ġof Ġclasses Ġ( 406 ), Ġoverall Ġraw Ġinter annot ator Ġagreement Ġbefore Ġresolution Ġwas Ġhigh Ġ( Co hen 's ĠIN LINE FORM 0 Ġ= Ġ0 . 58 ). ĠWhen Ġlabels Ġare Ġtrunc ated Ġto Ġa Ġmaximum Ġtax onomy Ġdepth Ġof ĠN , Ġraw Ġinter annot ator Ġincreases Ġto ĠIN LINE FORM 1 Ġ= Ġ0 . 85 Ġat Ġthe Ġco ars est Ġ( 9 Ġclass ) Ġlevel Ġ( see ĠTable ĠT AB REF 28 Ġ). ĠThis Ġis Ġconsidered Ġmoderate Ġto Ġstrong Ġagreement Ġ( see ĠMcH ugh ĠB IB REF 32 Ġfor Ġa Ġdiscussion Ġof Ġthe Ġinterpretation Ġof Ġthe ĠKappa Ġstatistic ). ĠBased Ġon Ġthe Ġresults Ġof Ġan Ġerror Ġanalysis Ġon Ġthe Ġquestion Ġclassification Ġsystem Ġ( see ĠSection ĠUID 38 Ġ), Ġwe Ġestimate Ġthat Ġthe Ġoverall Ġaccuracy Ġof Ġthe Ġquestion Ġclassification Ġlabels Ġafter Ġresolution Ġis Ġapproximately Ġ96 % Ġ. </s> An not ators Ġdisagreed Ġon Ġ3 441 Ġ( 44 . 2 %) Ġof Ġquestions . ĠPrimary Ġsources Ġof Ġdisagreement Ġbefore Ġresolution Ġincluded Ġeach Ġannot ator Ġchoosing Ġa Ġsingle Ġcategory Ġfor Ġquestions Ġrequiring Ġmultiple Ġlabels Ġ( e . g . Ġannot ator Ġ1 Ġassigning Ġa Ġlabel Ġof ĠX , Ġand Ġannot ator Ġ2 Ġassigning Ġa Ġlabel Ġof ĠY , Ġwhen Ġthe Ġgold Ġlabel Ġwas Ġmult il abel ĠX , ĠY ), Ġwhich Ġwas Ġobserved Ġin Ġ18 % Ġof Ġdisagreements . ĠSimilarly , Ġwe Ġobserved Ġannot ators Ġchoosing Ġsimilar Ġlabels Ġbut Ġat Ġdifferent Ġlevels Ġof Ġspecificity Ġin Ġthe Ġtax onomy Ġ( e . g . Ġannot ator Ġ1 Ġassigning Ġa Ġlabel Ġof ĠMatter ĠIN LINE FORM 0 ĠChanges Ġof ĠState ĠIN LINE FORM 1 ĠBo iling , Ġwhere Ġannot ator Ġ2 Ġassigned ĠMatter ĠIN LINE FORM 2 ĠChanges Ġof ĠState ), Ġwhich Ġoccurred Ġin Ġ12 % Ġof Ġdisagreements Ġbefore Ġresolution . </s> Question ĠClassification </s> Because Ġof Ġspace Ġlimitations Ġthe Ġquestion Ġclassification Ġresults Ġare Ġreported Ġin ĠTable ĠT AB REF 7 Ġonly Ġusing ĠMean ĠAverage ĠPrecision Ġ( MAP ). ĠWe Ġalso Ġinclude ĠPrecision @ 1 Ġ( P @ 1 ), Ġthe Ġoverall Ġaccuracy Ġof Ġthe Ġhighest - ranked Ġprediction Ġfor Ġeach Ġquestion Ġclassification Ġmodel , Ġin ĠTable ĠT AB REF 30 Ġ. </s> CNN : ĠWe Ġimplemented Ġthe ĠCNN Ġsentence Ġclass ifier Ġof ĠKim ĠB IB REF 28 Ġ, Ġwhich Ġdemonstrated Ġnear Ġstate - of - the - art Ġperformance Ġon Ġa Ġnumber Ġof Ġsentence Ġclassification Ġtasks Ġ( including ĠT REC Ġquestion Ġclassification ) Ġby Ġusing Ġpre - trained Ġword Ġembed d ings ĠB IB REF 40 Ġas Ġfeature Ġextract ors Ġin Ġa ĠCNN Ġmodel . ĠWe Ġadapted Ġthe Ġoriginal ĠCNN Ġnon - static Ġmodel Ġto Ġmulti - label Ġclassification Ġby Ġchanging Ġthe Ġfully Ġconnected Ġsoft max Ġlayer Ġto Ġs igm oid Ġlayer Ġto Ġproduce Ġa Ġs igm oid Ġoutput Ġfor Ġeach Ġlabel Ġsimultaneously . ĠWe Ġfollowed Ġthe Ġsame Ġparameter Ġsettings Ġreported Ġby ĠKim Ġet Ġal . Ġexcept Ġthe Ġlearning Ġrate , Ġwhich Ġwas Ġtuned Ġbased Ġon Ġthe Ġdevelopment Ġset . ĠPilot Ġexperiments Ġdid Ġnot Ġshow Ġa Ġperformance Ġimprovement Ġover Ġthe Ġbaseline Ġmodel . </s> Label ĠDefinitions : ĠQuestion Ġterms Ġcan Ġbe Ġmapped Ġto Ġcategories Ġusing Ġmanual Ġhe ur istics ĠB IB REF 19 Ġ. ĠTo Ġmitigate Ġsp arsity Ġand Ġlimit Ġhe uristic Ġuse , Ġhere Ġwe Ġgenerated Ġa Ġfeature Ġcomparing Ġthe Ġcos ine Ġsimilarity Ġof Ġcomposite Ġembed ding Ġvectors ĠB IB REF 51 Ġrepresenting Ġquestion Ġtext Ġand Ġcategory Ġdefinition Ġtext , Ġusing Ġpret rained ĠGl o Ve Ġembed d ings ĠB IB REF 52 Ġ. ĠPilot Ġexperiments Ġshowed Ġthat Ġperformance Ġdid Ġnot Ġsignificantly Ġimprove . </s> Question ĠExpansion Ġwith ĠHyper ny ms Ġ( Pro base ĠVersion ): ĠOne Ġof Ġthe Ġchallenges Ġof Ġhyper ny m Ġexpansion ĠB IB REF 22 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 3 Ġis Ġdetermining Ġa Ġhe uristic Ġfor Ġthe Ġtermination Ġdepth Ġof Ġhyper ny m Ġexpansion , Ġas Ġin ĠVan - tu Ġet Ġal . ĠB IB REF 24 Ġ. ĠBecause Ġscience Ġexam Ġquestions Ġare Ġoften Ġgrounded Ġin Ġspecific Ġexamples Ġ( e . g . Ġa Ġcar Ġrolling Ġdown Ġa Ġhill Ġcoming Ġto Ġa Ġstop Ġdue Ġto Ġfriction ), Ġwe Ġhypothesized Ġthat Ġknowing Ġcertain Ġcategories Ġof Ġentities Ġcan Ġbe Ġimportant Ġfor Ġidentifying Ġspecific Ġquestion Ġtypes ĠâĢĵ Ġfor Ġexample , Ġobserving Ġthat Ġa Ġquestion Ġcontains Ġa Ġkind Ġof Ġanimal Ġmay Ġbe Ġsuggestive Ġof Ġa ĠLife ĠScience Ġquestion , Ġwhere Ġsimilarly Ġvehicles Ġor Ġmaterials Ġpresent Ġin Ġquestions Ġmay Ġsuggest Ġquestions Ġabout ĠForces Ġor ĠMatter , Ġrespectively . ĠThe Ġchallenge Ġwith ĠWord Net Ġis Ġthat Ġkey Ġhyper ny ms Ġcan Ġbe Ġat Ġvery Ġdifferent Ġdepths Ġfrom Ġquery Ġterms ĠâĢĵ Ġfor Ġexample , ĠâĢ ľ cat âĢ Ŀ Ġis Ġdistance Ġ10 Ġaway Ġfrom Ġliving Ġthing , ĠâĢ ľ car âĢ Ŀ Ġis Ġdistance Ġ4 Ġaway Ġfrom Ġvehicle , Ġand ĠâĢ ľ co pper âĢ Ŀ Ġis Ġdistance Ġ2 Ġaway Ġfrom Ġmaterial . ĠCho osing Ġa Ġstatic Ġthreshold Ġ( or Ġdecaying Ġthreshold , Ġas Ġin ĠVan - tu Ġet Ġal . ĠB IB REF 24 Ġ) Ġwill Ġinher ient ly Ġreduce Ġrecall Ġand Ġlimit Ġthe Ġutility Ġof Ġthis Ġmethod Ġof Ġquery Ġexpansion . </s> To Ġaddress Ġthis , Ġwe Ġpil oted Ġa Ġhyper ny m Ġexpansion Ġexperiment Ġusing Ġthe ĠPro base Ġtax onomy ĠB IB REF 53 Ġ, Ġa Ġcollection Ġof Ġ20 . 7 M Ġis - a Ġpairs Ġmined Ġfrom Ġthe Ġweb , Ġin Ġplace Ġof ĠWord Net . ĠBecause Ġthe Ġtax onomic Ġpairs Ġin ĠPro base Ġcome Ġfrom Ġuse Ġin Ġnatural istic Ġsettings , Ġlinks Ġtend Ġto Ġjump Ġlevels Ġin Ġthe ĠWord Net Ġtax onomy Ġand Ġbe Ġexpressed Ġin Ġcommon Ġforms . ĠFor Ġexample , ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ, Ġand ĠIN LINE FORM 2 Ġ, Ġare Ġeach Ġdistance Ġ1 Ġin Ġthe ĠPro base Ġtax onomy , Ġand Ġhigh - frequency Ġ( i . e . Ġhigh - confidence ) Ġtax onomic Ġpairs . </s> Similar Ġto Ġquery Ġexpansion Ġusing ĠWord Net ĠHyper ny ms , Ġour Ġpilot Ġexperiments Ġdid Ġnot Ġobserve Ġa Ġbenefit Ġto Ġusing ĠPro base Ġhyper ny ms Ġover Ġthe Ġbaseline Ġmodel . ĠAn Ġerror Ġanalysis Ġsuggested Ġthat Ġthe Ġlarge Ġnumber Ġof Ġnoisy Ġand Ġout - of - context Ġlinks Ġpresent Ġin ĠPro base Ġmay Ġhave Ġreduced Ġperformance , Ġand Ġin Ġresponse Ġwe Ġconstructed Ġa Ġfiltered Ġlist Ġof Ġ7 10 Ġkey Ġhyper ny m Ġcategories Ġmanually Ġfiltered Ġfrom Ġa Ġlist Ġof Ġhyper ny ms Ġseeded Ġusing Ġhigh - frequency Ġwords Ġfrom Ġan Ġin - house Ġcorpus Ġof Ġ250 Ġin - domain Ġscience Ġtextbooks . ĠWe Ġalso Ġdid Ġnot Ġobserve Ġa Ġbenefit Ġto Ġquestion Ġclassification Ġover Ġthe Ġbaseline Ġmodel Ġwhen Ġexpanding Ġonly Ġto Ġthis Ġmanually Ġcurated Ġlist Ġof Ġkey Ġhyper ny ms . </s> Topic Ġwords : ĠWe Ġmade Ġuse Ġof Ġthe Ġ77 ĠT REC Ġword Ġlists Ġof ĠLi Ġand ĠRoth ĠB IB REF 6 Ġ, Ġcontaining Ġa Ġtotal Ġof Ġ3 , 257 Ġterms , Ġas Ġwell Ġas Ġan Ġin - house Ġset Ġof Ġ144 Ġword Ġlists Ġon Ġgeneral Ġand Ġelementary Ġscience Ġtopics Ġmined Ġfrom Ġthe Ġweb , Ġsuch Ġas ĠAN IM ALS , ĠV EG ET AB LES , Ġand ĠV EH IC LES , Ġcontaining Ġa Ġtotal Ġof Ġ29 , 059 Ġwords . ĠTo Ġmitigate Ġsp arsity , Ġfeatures Ġtake Ġthe Ġform Ġof Ġcounts Ġfor Ġa Ġspecific Ġtopic ĠâĢĵ Ġdetecting Ġthe Ġwords Ġturtle Ġand Ġgir affe Ġin Ġa Ġquestion Ġwould Ġprovide Ġa Ġcount Ġof Ġ2 Ġfor Ġthe ĠAN IM AL Ġfeature . ĠThis Ġprovides Ġa Ġlight Ġform Ġof Ġdomain - specific Ġentity Ġand Ġaction Ġ( e . g . Ġtypes Ġof Ġchanges ) Ġrecognition . ĠPilot Ġexperiments Ġshowed Ġthat Ġthis Ġword list Ġfeature Ġdid Ġadd Ġa Ġmodest Ġperformance Ġbenefit Ġof Ġapproximately Ġ2 % Ġto Ġquestion Ġclassification Ġaccuracy . ĠTaken Ġtogether Ġwith Ġour Ġresults Ġon Ġhyper ny m Ġexpansion , Ġthis Ġsuggests Ġthat Ġmanually Ġcurated Ġword lists Ġcan Ġshow Ġmodest Ġbenefits Ġfor Ġquestion Ġclassification Ġperformance , Ġbut Ġat Ġthe Ġexpense Ġof Ġsubstantial Ġeffort Ġin Ġauthor ing Ġor Ġcollecting Ġthese Ġextensive Ġword lists . </s> Hyper param eters : ĠFor Ġeach Ġlayer Ġof Ġthe Ġclass Ġlabel Ġhierarchy , Ġwe Ġtune Ġthe Ġhyper param eters Ġbased Ġon Ġthe Ġdevelopment Ġset . ĠWe Ġuse Ġthe Ġpret rained ĠB ERT - Base Ġ( unc ased ) Ġcheckpoint . ĠWe Ġuse Ġthe Ġfollowing Ġhyper param eters : Ġmaximum Ġsequence Ġlength Ġ= Ġ256 , Ġbatch Ġsize Ġ= Ġ16 , Ġlearning Ġrates : Ġ2 e - 5 Ġ( L 1 ), Ġ5 e - 5 Ġ( L 2 - L 6 ), Ġepoch s : Ġ5 Ġ( L 1 ), Ġ25 Ġ( L 2 - L 6 ). </s> Statistics : ĠWe Ġuse Ġnon - param etric Ġboot strap Ġres am pling Ġto Ġcompare Ġthe Ġbaseline Ġ( Li Ġand ĠRoth ĠB IB REF 6 Ġmodel ) Ġto Ġall Ġexperimental Ġmodels Ġto Ġdetermine Ġsignificance , Ġusing Ġ10 , 000 Ġboot strap Ġres amples .
Introduction Understanding what a question is asking is one of the first steps that humans use to work towards an answer. In the context of question answering, question classification allows automated systems to intelligently target their inference systems to domain-specific solvers capable of addressing specific kinds of questions and problem solving methods with high confidence and answer accuracy BIBREF0 , BIBREF1 . To date, question classification has primarily been studied in the context of open-domain TREC questions BIBREF2 , with smaller recent datasets available in the biomedical BIBREF3 , BIBREF4 and education BIBREF5 domains. The open-domain TREC question corpus is a set of 5,952 short factoid questions paired with a taxonomy developed by Li and Roth BIBREF6 that includes 6 coarse answer types (such as entities, locations, and numbers), and 50 fine-grained types (e.g. specific kinds of entities, such as animals or vehicles). While a wide variety of syntactic, semantic, and other features and classification methods have been applied to this task, culminating in near-perfect classification performance BIBREF7 , recent work has demonstrated that QC methods developed on TREC questions generally fail to transfer to datasets with more complex questions such as those in the biomedical domain BIBREF3 , likely due in part to the simplicity and syntactic regularity of the questions, and the ability for simpler term-frequency models to achieve near-ceiling performance BIBREF8 . In this work we explore question classification in the context of multiple choice science exams. Standardized science exams have been proposed as a challenge task for question answering BIBREF9 , as most questions contain a variety of challenging inference problems BIBREF10 , BIBREF11 , require detailed scientific and common-sense knowledge to answer and explain the reasoning behind those answers BIBREF12 , and questions are often embedded in complex examples or other distractors. Question classification taxonomies and annotation are difficult and expensive to generate, and because of the unavailability of this data, to date most models for science questions use one or a small number of generic solvers that perform little or no question decomposition BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 . Our long-term interest is in developing methods that intelligently target their inferences to generate both correct answers and compelling human-readable explanations for the reasoning behind those answers. The lack of targeted solving – using the same methods for inferring answers to spatial questions about planetary motion, chemical questions about photosynthesis, and electrical questions about circuit continuity – is a substantial barrier to increasing performance (see Figure FIGREF1 ). To address this need for developing methods of targetted inference, this work makes the following contributions: Related work Question classification typically makes use of a combination of syntactic, semantic, surface, and embedding methods. Syntactic patterns BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 and syntactic dependencies BIBREF3 have been shown to improve performance, while syntactically or semantically important words are often expanding using Wordnet hypernyms or Unified Medical Language System categories (for the medical domain) to help mitigate sparsity BIBREF22 , BIBREF23 , BIBREF24 . Keyword identification helps identify specific terms useful for classification BIBREF25 , BIBREF3 , BIBREF26 . Similarly, named entity recognizers BIBREF6 , BIBREF27 or lists of semantically related words BIBREF6 , BIBREF24 can also be used to establish broad topics or entity categories and mitigate sparsity, as can word embeddings BIBREF28 , BIBREF29 . Here, we empirically demonstrate many of these existing methods do not transfer to the science domain. The highest performing question classification systems tend to make use of customized rule-based pattern matching BIBREF30 , BIBREF7 , or a combination of rule-based and machine learning approaches BIBREF19 , at the expense of increased model construction time. A recent emphasis on learned methods has shown a large set of CNN BIBREF29 and LSTM BIBREF8 variants achieve similar accuracy on TREC question classification, with these models exhibiting at best small gains over simple term frequency models. These recent developments echo the observations of Roberts et al. BIBREF3 , who showed that existing methods beyond term frequency models failed to generalize to medical domain questions. Here we show that strong performance across multiple datasets is possible using a single learned model. Due to the cost involved in their construction, question classification datasets and classification taxonomies tend to be small, which can create methodological challenges. Roberts et al. BIBREF3 generated the next-largest dataset from TREC, containing 2,936 consumer health questions classified into 13 question categories. More recently, Wasim et al. BIBREF4 generated a small corpus of 780 biomedical domain questions organized into 88 categories. In the education domain, Godea et al. BIBREF5 collected a set of 1,155 classroom questions and organized these into 16 categories. To enable a detailed study of science domain question classification, here we construct a large-scale challenge dataset that exceeds the size and classification specificity of other datasets, in many cases by nearly an order of magnitude. Questions and Classification Taxonomy Questions: We make use of the 7,787 science exam questions of the Aristo Reasoning Challenge (ARC) corpus BIBREF31 , which contains standardized 3rd to 9th grade science questions from 12 US states from the past decade. Each question is a 4-choice multiple choice question. Summary statistics comparing the complexity of ARC and TREC questions are shown in Table TABREF5 . Taxonomy: Starting with the syllabus for the NY Regents exam, we identified 9 coarse question categories (Astronomy, Earth Science, Energy, Forces, Life Science, Matter, Safety, Scientific Method, Other), then through a data-driven analysis of 3 exam study guides and the 3,370 training questions, expanded the taxonomy to include 462 fine-grained categories across 6 hierarchical levels of granularity. The taxonomy is designed to allow categorizing questions into broad curriculum topics at it's coarsest level, while labels at full specificity separate questions into narrow problem domains suitable for targetted inference methods. Because of its size, a subset of the classification taxonomy is shown in Table TABREF6 , with the full taxonomy and class definitions included in the supplementary material. Annotation: Because of the complexity of the questions, it is possible for one question to bridge multiple categories – for example, a wind power generation question may span both renewable energy and energy conversion. We allow up to 2 labels per question, and found that 16% of questions required multiple labels. Each question was independently annotated by two annotators, with the lead annotator a domain expert in standardized exams. Annotators first independently annotated the entire question set, then questions without complete agreement were discussed until resolution. Before resolution, interannotator agreement (Cohen's Kappa) was INLINEFORM0 = 0.58 at the finest level of granularity, and INLINEFORM1 = 0.85 when considering only the coarsest 9 categories. This is considered moderate to strong agreement BIBREF32 . Based on the results of our error analysis (see Section SECREF21 ), we estimate the overall accuracy of the question classification labels after resolution to be approximately 96%. While the full taxonomy contains 462 fine-grained categories derived from both standardized questions, study guides, and exam syllabi, we observed only 406 of these categories are tested in the ARC question set. Question Classification on Science Exams We identified 5 common models in previous work primarily intended for learned classifiers rather than hand-crafted rules. We adapt these models to a multi-label hierarchical classification task by training a series of one-vs-all binary classifiers BIBREF34 , one for each label in the taxonomy. With the exception of the CNN and BERT models, following previous work BIBREF19 , BIBREF3 , BIBREF8 we make use of an SVM classifier using the LIBSvM framework BIBREF35 with a linear kernel. Models are trained and evaluated from coarse to fine levels of taxonomic specificity. At each level of taxonomic evaluation, a set of non-overlapping confidence scores for each binary classifier are generated and sorted to produce a list of ranked label predictions. We evaluate these ranks using Mean Average Precision BIBREF36 . ARC questions are evaluated using the standard 3,370 questions for training, 869 for development, and 3,548 for testing. N-grams, POS, Hierarchical features: A baseline bag-of-words model incorporating both tagged and untagged unigrams and bigams. We also implement the hierarchical classification feature of Li and Roth BIBREF6 , where for a given question, the output of the classifier at coarser levels of granularity serves as input to the classifier at the current level of granularity. Dependencies: Bigrams of Stanford dependencies BIBREF37 . For each word, we create one unlabeled bigram for each outgoing link from that word to it's dependency BIBREF20 , BIBREF3 . Question Expansion with Hypernyms: We perform hypernym expansion BIBREF22 , BIBREF19 , BIBREF3 by including WordNet hypernyms BIBREF38 for the root dependency word, and words on it's direct outgoing links. WordNet sense is identified using Lesk word-sense disambiguation BIBREF39 , using question text for context. We implement the heuristic of Van-tu et al. BIBREF24 , where more distant hypernyms receive less weight. Essential Terms: Though not previously reported for QC, we make use of unigrams of keywords extracted using the Science Exam Essential Term Extractor of Khashabi et al. BIBREF26 . For each keyword, we create one binary unigram feature. CNN: Kim BIBREF28 demonstrated near state-of-the-art performance on a number of sentence classification tasks (including TREC question classification) by using pre-trained word embeddings BIBREF40 as feature extractors in a CNN model. Lei et al. BIBREF29 showed that 10 CNN variants perform within +/-2% of Kim's BIBREF28 model on TREC QC. We report performance of our best CNN model based on the MP-CNN architecture of Rao et al. BIBREF41 , which works to establish the similarity between question text and the definition text of the question classes. We adapt the MP-CNN model, which uses a “Siamese” structure BIBREF33 , to create separate representations for both the question and the question class. The model then makes use of a triple ranking loss function to minimize the distance between the representations of questions and the correct class while simultaneously maximising the distance between questions and incorrect classes. We optimize the network using the method of Tu BIBREF42 . BERT-QC (This work): We make use of BERT BIBREF43 , a language model using bidirectional encoder representations from transformers, in a sentence-classification configuration. As the original settings of BERT do not support multi-label classification scenarios, and training a series of 406 binary classifiers would be computationally expensive, we use the duplication method of Tsoumakas et al. BIBREF34 where we enumerate multi-label questions as multiple single-label instances during training by duplicating question text, and assigning each instance one of the multiple labels. Evaluation follows the standard procedure where we generate a list of ranked class predictions based on class probabilities, and use this to calculate Mean Average Precision (MAP) and Precision@1 (P@1). As shown in Table TABREF7 , this BERT-QC model achieves our best question classification performance, significantly exceeding baseline performance on ARC by 0.12 MAP and 13.5% P@1. Comparison with Benchmark Datasets Apart from term frequency methods, question classification methods developed on one dataset generally do not exhibit strong transfer performance to other datasets BIBREF3 . While BERT-QC achieves large gains over existing methods on the ARC dataset, here we demonstrate that BERT-QC also matches state-of-the-art performance on TREC BIBREF6 , while surpassing state-of-the-art performance on the GARD corpus of consumer health questions BIBREF3 and MLBioMedLAT corpus of biomedical questions BIBREF4 . As such, BERT-QC is the first model to achieve strong performance across more than one question classification dataset. TREC question classification is divided into separate coarse and fine-grained tasks centered around inferring the expected answer types of short open-domain factoid questions. TREC-6 includes 6 coarse question classes (abbreviation, entity, description, human, location, numeric), while TREC-50 expands these into 50 more fine-grained types. TREC question classification methods can be divided into those that learn the question classification task, and those that make use of either hand-crafted or semi-automated syntactic or semantic extraction rules to infer question classes. To date, the best reported accuracy for learned methods is 98.0% by Xia et al. BIBREF8 for TREC-6, and 91.6% by Van-tu et al. BIBREF24 for TREC-50. Madabushi et al. BIBREF7 achieve the highest to-date performance on TREC-50 at 97.2%, using rules that leverage the strong syntactic regularities in the short TREC factoid questions. We compare the performance of BERT-QC with recently reported performance on this dataset in Table TABREF11 . BERT-QC achieves state-of-the-art performance on fine-grained classification (TREC-50) for a learned model at 92.0% accuracy, and near state-of-the-art performance on coarse classification (TREC-6) at 96.2% accuracy. Because of the challenges with collecting biomedical questions, the datasets and classification taxonomies tend to be small, and rule-based methods often achieve strong results BIBREF45 . Roberts et al. BIBREF3 created the largest biomedical question classification dataset to date, annotating 2,937 consumer health questions drawn from the Genetic and Rare Diseases (GARD) question database with 13 question types, such as anatomy, disease cause, diagnosis, disease management, and prognoses. Roberts et al. BIBREF3 found these questions largely resistant to learning-based methods developed for TREC questions. Their best model (CPT2), shown in Table TABREF17 , makes use of stemming and lists of semantically related words and cue phrases to achieve 80.4% accuracy. BERT-QC reaches 84.9% accuracy on this dataset, an increase of +4.5% over the best previous model. We also compare performance on the recently released MLBioMedLAT dataset BIBREF4 , a multi-label biomedical question classification dataset with 780 questions labeled using 88 classification types drawn from 133 Unified Medical Language System (UMLS) categories. Table TABREF18 shows BERT-QC exceeds their best model, focus-driven semantic features (FDSF), by +0.05 Micro-F1 and +3% accuracy. Error Analysis We performed an error analysis on 50 ARC questions where the BERT-QC system did not predict the correct label, with a summary of major error categories listed in Table TABREF20 . Associative Errors: In 35% of cases, predicted labels were nearly correct, differing from the correct label only by the finest-grained (leaf) element of the hierarchical label (for example, predicting Matter INLINEFORM0 Changes of State INLINEFORM1 Boiling instead of Matter INLINEFORM2 Changes of State INLINEFORM3 Freezing). The bulk of the remaining errors were due to questions containing highly correlated words with a different class, or classes themselves being highly correlated. For example, a specific question about Weather Models discusses “environments” changing over “millions of years”, where discussions of environments and long time periods tend to be associated with questions about Locations of Fossils. Similarly, a question containing the word “evaporation” could be primarily focused on either Changes of State or the Water Cycle (cloud generation), and must rely on knowledge from the entire question text to determine the correct problem domain. We believe these associative errors are addressable technical challenges that could ultimately lead to increased performance in subsequent models. Errors specific to the multiple-choice domain: We observed that using both question and all multiple choice answer text produced large gains in question classification performance – for example, BERT-QC performance increases from 0.516 (question only) to 0.654 (question and all four answer candidates), an increase of 0.138 MAP. Our error analysis observed that while this substantially increases QC performance, it changes the distribution of errors made by the system. Specifically, 25% of errors become highly correlated with an incorrect answer candidate, which (we show in Section SECREF5 ) can reduce the performance of QA solvers. Question Answering with QC Labels Because of the challenges of errorful label predictions correlating with incorrect answers, it is difficult to determine the ultimate benefit a QA model might receive from reporting QC performance in isolation. Coupling QA and QC systems can often be laborious – either a large number of independent solvers targeted to specific question types must be constructed BIBREF46 , or an existing single model must be able to productively incorporate question classification information. Here we demostrate the latter – that a BERT QA model is able to incorporate question classification information through query expansion. BERT BIBREF43 recently demonstrated state-of-the-art performance on benchmark question answering datasets such as SQUaD BIBREF47 , and near human-level performance on SWAG BIBREF48 . Similarly, Pan et al. BIBREF49 demonstrated that BERT achieves the highest accuracy on the most challenging subset of ARC science questions. We make use of a BERT QA model using the same QA paradigm described by Pan et al. BIBREF49 , where QA is modeled as a next-sentence prediction task that predicts the likelihood of a given multiple choice answer candidate following the question text. We evaluate the question text and the text of each multiple choice answer candidate separately, where the answer candidate with the highest probablity is selected as the predicted answer for a given question. Performance is evaluated using Precision@1 BIBREF36 . Additional model details and hyperparameters are included in the Appendix. We incorporate QC information into the QA process by implementing a variant of a query expansion model BIBREF50 . Specifically, for a given {question, QC_label} pair, we expand the question text by concatenating the definition text of the question classification label to the start of the question. We use of the top predicted question classification label for each question. Because QC labels are hierarchical, we append the label definition text for each level of the label INLINEFORM0 . An exampe of this process is shown in Table TABREF23 . Figure FIGREF24 shows QA peformance using predicted labels from the BERT-QC model, compared to a baseline model that does not contain question classification information. As predicted by the error analysis, while a model trained with question and answer candidate text performs better at QC than a model using question text alone, a large proportion of the incorrect predictions become associated with a negative answer candidate, reducing overall QA performance, and highlighting the importance of evaluating QC and QA models together. When using BERT-QC trained on question text alone, at the finest level of specificity (L6) where overall question classification accuracy is 57.8% P@1, question classification significantly improves QA performance by +1.7% P@1 INLINEFORM0 . Using gold labels shows ceiling QA performance can reach +10.0% P@1 over baseline, demonstrating that as question classification model performance improves, substantial future gains are possible. An analysis of expected gains for a given level of QC performance is included in the Appendix, showing approximately linear gains in QA performance above baseline for QC systems able to achieve over 40% classification accuracy. Below this level, the decreased performance from noise induced by incorrect labels surpasses gains from correct labels. Hyperparameters: Pilot experiments on both pre-trained BERT-Base and BERT-Large checkpoints showed similar performance benefits at the finest levels of question classification granularity (L6), but the BERT-Large model demonstrated higher overall baseline performance, and larger incremental benefits at lower levels of QC granularity, so we evaluated using that model. We lightly tuned hyperparameters on the development set surrounding those reported by Devlin et al. BIBREF43 , and ultimately settled on parameters similar to their original work, tempered by technical limitations in running the BERT-Large model on available hardware: maximum sequence length = 128, batch size = 16, learning rate: 1e-5. We report performance as the average of 10 runs for each datapoint. The number of epochs were tuned on each run on the development set (to a maximum of 8 epochs), where most models converged to maximum performance within 5 epochs. Preference for uncorrelated errors in multiple choice question classification: We primarily report QA performance using BERT-QC trained using text from only the multiple choice questions and not their answer candidates. While this model achieved lower overall QC performance compared to the model trained with both question and multiple choice answer candidate text, it achieved slightly higher performance in the QA+QC setting. Our error analysis in Section SECREF21 shows that though models trained on both question and answer text can achieve higher QC performance, when they make QC errors, the errors tend to be highly correlated with an incorrect answer candidate, which can substantially reduce QA performance. This is an important result for question classification in the context of multiple choice exams.In the context of multiple choice exams, correlated noise can substantially reduce QA performance, meaning the kinds of errors that a model makes are important, and evaluating QC performance in context with QA models that make use of those QC systems is critical. Related to this result, we provide an analysis of the noise sensitivity of the QA+QC model for different levels of question classification prediction accuracy. Here, we perturb gold question labels by randomly selecting a proportion of questions (between 5% and 40%) and randomly assigning that question a different label. Figure FIGREF36 shows that this uncorrelated noise provides roughly linear decreases in performance, and still shows moderate gains at 60% accuracy (40% noise) with uncorrelated noise. This suggests that when making errors, making random errors (that are not correlated to incorrect multiple choice answers) is preferential. Training with predicted labels: We observed small gains when training the BERT-QA model with predicted QC labels. We generate predicted labels for the training set using 5-fold crossvalidation over only the training questions. Statistics: We use non-parametric bootstrap resampling to compare baseline (no label) and experimental (QC labeled) runs of the QA+QC experiment. Because the BERT-QA model produces different performance values across successive runs, we perform 10 runs of each condition. We then compute pairwise p-values for each of the 10 no label and QC labeled runs (generating 100 comparisons), then use Fisher's method to combine these into a final statistic. Question classification paired with question answering shows statistically significant gains of +1.7% P@1 at L6 using predicted labels, and a ceiling gain of up to +10% P@1 using gold labels. The QA performance graph in Figure FIGREF24 contains two deviations from the expectation of linear gains with increasing specificity, at L1 and L3. Region at INLINEFORM0 On gold labels, L3 provides small gains over L2, where as L4 provides large gains over L3. We hypothesize that this is because approximately 57% of question labels belong to the Earth Science or Life Science categories which have much more depth than breadth in the standardized science curriculum, and as such these categories are primarily differentiated from broad topics into detailed problem types at levels L4 through L6. Most other curriculum categories have more breadth than depth, and show strong (but not necessarily full) differentiation at L2. Region at INLINEFORM1 Predicted performance at L1 is higher than gold performance at L1. We hypothesize this is because we train using predicted rather than gold labels, which provides a boost in performance. Training on gold labels and testing on predicted labels substantially reduces the difference between gold and predicted performance. Though initial raw interannotator agreement was measured at INLINEFORM0 , to maximize the quality of the annotation the annotators performed a second pass where all disagreements were manually resolved. Table TABREF30 shows question classification performance of the BERT-QC model at 57.8% P@1, meaning 42.2% of the predicted labels were different than the gold labels. The question classification error analysis in Table TABREF20 found that of these 42.2% of errorful predictions, 10% of errors (4.2% of total labels) were caused by the gold labels being incorrect. This allows us to estimate that the overall quality of the annotation (the proportion of questions that have a correct human authored label) is approximately 96%. Automating Error Analyses with QC Detailed error analyses for question answering systems are typically labor intensive, often requiring hours or days to perform manually. As a result error analyses are typically completed infrequently, in spite of their utility to key decisions in the algortithm or knowledge construction process. Here we show having access to detailed question classification labels specifying fine-grained problem domains provides a mechanism to automatically generate error analyses in seconds instead of days. To illustrate the utility of this approach, Table TABREF26 shows the performance of the BERT QA+QC model broken down by specific question classes. This allows automatically identifying a given model's strengths – for example, here questions about Human Health, Material Properties, and Earth's Inner Core are well addressed by the BERT-QA model, and achieve well above the average QA performance of 49%. Similarly, areas of deficit include Changes of State, Reproduction, and Food Chain Processes questions, which see below-average QA performance. The lowest performing class, Safety Procedures, demonstrates that while this model has strong performance in many areas of scientific reasoning, it is worse than chance at answering questions about safety, and would be inappropriate to deploy for safety-critical tasks. While this analysis is shown at an intermediate (L2) level of specificity for space, more detailed analyses are possible. For example, overall QA performance on Scientific Inference questions is near average (47%), but increasing granularity to L3 we observe that questions addressing Experiment Design or Making Inferences – challenging questions even for humans – perform poorly (33% and 20%) when answered by the QA system. This allows a system designer to intelligently target problem-specific knowledge resources and inference methods to address deficits in specific areas. Conclusion Question classification can enable targetting question answering models, but is challenging to implement with high performance without using rule-based methods. In this work we generate the most fine-grained challenge dataset for question classification, using complex and syntactically diverse questions, and show gains of up to 12% are possible with our question classification model across datasets in open, science, and medical domains. This model is the first demonstration of a question classification model achieving state-of-the-art results across benchmark datasets in open, science, and medical domains. We further demonstrate attending to question type can significantly improve question answering performance, with large gains possible as quesion classification performance improves. Our error analysis suggests that developing high-precision methods of question classification independent of their recall can offer the opportunity to incrementally make use of the benefits of question classification without suffering the consequences of classification errors on QA performance. Resources Our Appendix and supplementary material (available at http://www.cognitiveai.org/explanationbank/) includes data, code, experiment details, and negative results. Acknowledgements The authors wish to thank Elizabeth Wainwright and Stephen Marmorstein for piloting an earlier version of the question classification annotation. We thank the Allen Insitute for Artificial Intelligence and National Science Founation (NSF 1815948 to PJ) for funding this work. Annotation Classification Taxonomy: The full classification taxonomy is included in separate files, both coupled with definitions, and as a graphical visualization. Annotation Procedure: Primary annotation took place over approximately 8 weeks. Annotators were instructed to provide up to 2 labels from the full classification taxonomy (462 labels) that were appropriate for each question, and to provide the most specific label available in the taxonomy for a given question. Of the 462 labels in the classification taxonomy, the ARC questions had non-zero counts in 406 question types. Rarely, questions were encountered by annotators that did not clearly fit into a label at the end of the taxonomy, and in these cases the annotators were instructed to choose a more generic label higher up the taxonomy that was appropriate. This occurred when the production taxonomy failed to have specific categories for infrequent questions testing knowledge that is not a standard part of the science curriculum. For example, the question: Which material is the best natural resource to use for making water-resistant shoes? (A) cotton (B) leather (C) plastic (D) wool tests a student's knowledge of the water resistance of different materials. Because this is not a standard part of the curriculum, and wasn't identified as a common topic in the training questions, the annotators tag this question as belonging to Matter INLINEFORM0 Properties of Materials, rather than a more specific category. Questions from the training, development, and test sets were randomly shuffled to counterbalance any learning effects during the annotation procedure, but were presented in grade order (3rd to 9th grade) to reduce context switching (a given grade level tends to use a similar subset of the taxonomy – for example, 3rd grade questions generally do not address Chemical Equations or Newtons 1st Law of Motion). Interannotator Agreement: To increase quality and consistency, each annotator annotated the entire dataset of 7,787 questions. Two annotators were used, with the lead annotator possessing previous professional domain expertise. Annotation proceeded in a two-stage process, where in stage 1 annotators completed their annotation independently, and in stage 2 each of the questions where the annotators did not have complete agreement were manually resolved by the annotators, resulting in high-quality classification annotation. Because each question can have up to two labels, we treat each label for a given question as a separate evaluation of interannotator agreement. That is, for questions where both annotators labeled each question as having 1 or 2 labels, we treat this as 1 or 2 separate evaluations of interannotator agreement. For cases where one annotator labeled as question as having 1 label, and the other annotator labeled that same question as having 2 labels, we conservatively treat this as two separate interannotator agreements where one annotator failed to specify the second label and had zero agreement on that unspecified label. Though the classification procedure was fine-grained compared to other question classification taxonomies, containing an unusually large number of classes (406), overall raw interannotator agreement before resolution was high (Cohen's INLINEFORM0 = 0.58). When labels are truncated to a maximum taxonomy depth of N, raw interannotator increases to INLINEFORM1 = 0.85 at the coarsest (9 class) level (see Table TABREF28 ). This is considered moderate to strong agreement (see McHugh BIBREF32 for a discussion of the interpretation of the Kappa statistic). Based on the results of an error analysis on the question classification system (see Section UID38 ), we estimate that the overall accuracy of the question classification labels after resolution is approximately 96% . Annotators disagreed on 3441 (44.2%) of questions. Primary sources of disagreement before resolution included each annotator choosing a single category for questions requiring multiple labels (e.g. annotator 1 assigning a label of X, and annotator 2 assigning a label of Y, when the gold label was multilabel X, Y), which was observed in 18% of disagreements. Similarly, we observed annotators choosing similar labels but at different levels of specificity in the taxonomy (e.g. annotator 1 assigning a label of Matter INLINEFORM0 Changes of State INLINEFORM1 Boiling, where annotator 2 assigned Matter INLINEFORM2 Changes of State), which occurred in 12% of disagreements before resolution. Question Classification Because of space limitations the question classification results are reported in Table TABREF7 only using Mean Average Precision (MAP). We also include Precision@1 (P@1), the overall accuracy of the highest-ranked prediction for each question classification model, in Table TABREF30 . CNN: We implemented the CNN sentence classifier of Kim BIBREF28 , which demonstrated near state-of-the-art performance on a number of sentence classification tasks (including TREC question classification) by using pre-trained word embeddings BIBREF40 as feature extractors in a CNN model. We adapted the original CNN non-static model to multi-label classification by changing the fully connected softmax layer to sigmoid layer to produce a sigmoid output for each label simultaneously. We followed the same parameter settings reported by Kim et al. except the learning rate, which was tuned based on the development set. Pilot experiments did not show a performance improvement over the baseline model. Label Definitions: Question terms can be mapped to categories using manual heuristics BIBREF19 . To mitigate sparsity and limit heuristic use, here we generated a feature comparing the cosine similarity of composite embedding vectors BIBREF51 representing question text and category definition text, using pretrained GloVe embeddings BIBREF52 . Pilot experiments showed that performance did not significantly improve. Question Expansion with Hypernyms (Probase Version): One of the challenges of hypernym expansion BIBREF22 , BIBREF19 , BIBREF3 is determining a heuristic for the termination depth of hypernym expansion, as in Van-tu et al. BIBREF24 . Because science exam questions are often grounded in specific examples (e.g. a car rolling down a hill coming to a stop due to friction), we hypothesized that knowing certain categories of entities can be important for identifying specific question types – for example, observing that a question contains a kind of animal may be suggestive of a Life Science question, where similarly vehicles or materials present in questions may suggest questions about Forces or Matter, respectively. The challenge with WordNet is that key hypernyms can be at very different depths from query terms – for example, “cat” is distance 10 away from living thing, “car” is distance 4 away from vehicle, and “copper” is distance 2 away from material. Choosing a static threshold (or decaying threshold, as in Van-tu et al. BIBREF24 ) will inheriently reduce recall and limit the utility of this method of query expansion. To address this, we piloted a hypernym expansion experiment using the Probase taxonomy BIBREF53 , a collection of 20.7M is-a pairs mined from the web, in place of WordNet. Because the taxonomic pairs in Probase come from use in naturalistic settings, links tend to jump levels in the WordNet taxonomy and be expressed in common forms. For example, INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 , are each distance 1 in the Probase taxonomy, and high-frequency (i.e. high-confidence) taxonomic pairs. Similar to query expansion using WordNet Hypernyms, our pilot experiments did not observe a benefit to using Probase hypernyms over the baseline model. An error analysis suggested that the large number of noisy and out-of-context links present in Probase may have reduced performance, and in response we constructed a filtered list of 710 key hypernym categories manually filtered from a list of hypernyms seeded using high-frequency words from an in-house corpus of 250 in-domain science textbooks. We also did not observe a benefit to question classification over the baseline model when expanding only to this manually curated list of key hypernyms. Topic words: We made use of the 77 TREC word lists of Li and Roth BIBREF6 , containing a total of 3,257 terms, as well as an in-house set of 144 word lists on general and elementary science topics mined from the web, such as ANIMALS, VEGETABLES, and VEHICLES, containing a total of 29,059 words. To mitigate sparsity, features take the form of counts for a specific topic – detecting the words turtle and giraffe in a question would provide a count of 2 for the ANIMAL feature. This provides a light form of domain-specific entity and action (e.g. types of changes) recognition. Pilot experiments showed that this wordlist feature did add a modest performance benefit of approximately 2% to question classification accuracy. Taken together with our results on hypernym expansion, this suggests that manually curated wordlists can show modest benefits for question classification performance, but at the expense of substantial effort in authoring or collecting these extensive wordlists. Hyperparameters: For each layer of the class label hierarchy, we tune the hyperparameters based on the development set. We use the pretrained BERT-Base (uncased) checkpoint. We use the following hyperparameters: maximum sequence length = 256, batch size = 16, learning rates: 2e-5 (L1), 5e-5 (L2-L6), epochs: 5 (L1), 25 (L2-L6). Statistics: We use non-parametric bootstrap resampling to compare the baseline (Li and Roth BIBREF6 model) to all experimental models to determine significance, using 10,000 bootstrap resamples.
Which datasets are used for evaluation?
a3bb9a936f61bafb509fa12ac0a61f91abcc5106
Which Ġdatasets Ġare Ġused Ġfor Ġevaluation ?
ARC Ġ, ĠT REC , ĠG ARD Ġ, ĠMLB io Med L AT Ġ
<s> Which Ġdatasets Ġare Ġused Ġfor Ġevaluation ? </s> Introduction </s> Understanding Ġwhat Ġa Ġquestion Ġis Ġasking Ġis Ġone Ġof Ġthe Ġfirst Ġsteps Ġthat Ġhumans Ġuse Ġto Ġwork Ġtowards Ġan Ġanswer . ĠIn Ġthe Ġcontext Ġof Ġquestion Ġanswering , Ġquestion Ġclassification Ġallows Ġautomated Ġsystems Ġto Ġintellig ently Ġtarget Ġtheir Ġinference Ġsystems Ġto Ġdomain - specific Ġsol vers Ġcapable Ġof Ġaddressing Ġspecific Ġkinds Ġof Ġquestions Ġand Ġproblem Ġsolving Ġmethods Ġwith Ġhigh Ġconfidence Ġand Ġanswer Ġaccuracy ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ. </s> To Ġdate , Ġquestion Ġclassification Ġhas Ġprimarily Ġbeen Ġstudied Ġin Ġthe Ġcontext Ġof Ġopen - domain ĠT REC Ġquestions ĠB IB REF 2 Ġ, Ġwith Ġsmaller Ġrecent Ġdatasets Ġavailable Ġin Ġthe Ġbiomedical ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġand Ġeducation ĠB IB REF 5 Ġdomains . ĠThe Ġopen - domain ĠT REC Ġquestion Ġcorpus Ġis Ġa Ġset Ġof Ġ5 , 952 Ġshort Ġfact oid Ġquestions Ġpaired Ġwith Ġa Ġtax onomy Ġdeveloped Ġby ĠLi Ġand ĠRoth ĠB IB REF 6 Ġthat Ġincludes Ġ6 Ġcoarse Ġanswer Ġtypes Ġ( such Ġas Ġentities , Ġlocations , Ġand Ġnumbers ), Ġand Ġ50 Ġfine - gr ained Ġtypes Ġ( e . g . Ġspecific Ġkinds Ġof Ġentities , Ġsuch Ġas Ġanimals Ġor Ġvehicles ). ĠWhile Ġa Ġwide Ġvariety Ġof Ġsynt actic , Ġsemantic , Ġand Ġother Ġfeatures Ġand Ġclassification Ġmethods Ġhave Ġbeen Ġapplied Ġto Ġthis Ġtask , Ġculminating Ġin Ġnear - perfect Ġclassification Ġperformance ĠB IB REF 7 Ġ, Ġrecent Ġwork Ġhas Ġdemonstrated Ġthat ĠQC Ġmethods Ġdeveloped Ġon ĠT REC Ġquestions Ġgenerally Ġfail Ġto Ġtransfer Ġto Ġdatasets Ġwith Ġmore Ġcomplex Ġquestions Ġsuch Ġas Ġthose Ġin Ġthe Ġbiomedical Ġdomain ĠB IB REF 3 Ġ, Ġlikely Ġdue Ġin Ġpart Ġto Ġthe Ġsimplicity Ġand Ġsynt actic Ġregular ity Ġof Ġthe Ġquestions , Ġand Ġthe Ġability Ġfor Ġsimpler Ġterm - frequency Ġmodels Ġto Ġachieve Ġnear - ce iling Ġperformance ĠB IB REF 8 Ġ. ĠIn Ġthis Ġwork Ġwe Ġexplore Ġquestion Ġclassification Ġin Ġthe Ġcontext Ġof Ġmultiple Ġchoice Ġscience Ġexams . ĠStandard ized Ġscience Ġexams Ġhave Ġbeen Ġproposed Ġas Ġa Ġchallenge Ġtask Ġfor Ġquestion Ġanswering ĠB IB REF 9 Ġ, Ġas Ġmost Ġquestions Ġcontain Ġa Ġvariety Ġof Ġchallenging Ġinference Ġproblems ĠB IB REF 10 Ġ, ĠB IB REF 11 Ġ, Ġrequire Ġdetailed Ġscientific Ġand Ġcommon - sense Ġknowledge Ġto Ġanswer Ġand Ġexplain Ġthe Ġreasoning Ġbehind Ġthose Ġanswers ĠB IB REF 12 Ġ, Ġand Ġquestions Ġare Ġoften Ġembedded Ġin Ġcomplex Ġexamples Ġor Ġother Ġdistract ors . ĠQuestion Ġclassification Ġtax onom ies Ġand Ġannotation Ġare Ġdifficult Ġand Ġexpensive Ġto Ġgenerate , Ġand Ġbecause Ġof Ġthe Ġun availability Ġof Ġthis Ġdata , Ġto Ġdate Ġmost Ġmodels Ġfor Ġscience Ġquestions Ġuse Ġone Ġor Ġa Ġsmall Ġnumber Ġof Ġgeneric Ġsol vers Ġthat Ġperform Ġlittle Ġor Ġno Ġquestion Ġdecom position ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ, ĠB IB REF 15 Ġ, ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ. ĠOur Ġlong - term Ġinterest Ġis Ġin Ġdeveloping Ġmethods Ġthat Ġintellig ently Ġtarget Ġtheir Ġinf erences Ġto Ġgenerate Ġboth Ġcorrect Ġanswers Ġand Ġcompelling Ġhuman - readable Ġexplanations Ġfor Ġthe Ġreasoning Ġbehind Ġthose Ġanswers . ĠThe Ġlack Ġof Ġtargeted Ġsolving ĠâĢĵ Ġusing Ġthe Ġsame Ġmethods Ġfor Ġinfer ring Ġanswers Ġto Ġspatial Ġquestions Ġabout Ġplanetary Ġmotion , Ġchemical Ġquestions Ġabout Ġphotos ynthesis , Ġand Ġelectrical Ġquestions Ġabout Ġcircuit Ġcontinuity ĠâĢĵ Ġis Ġa Ġsubstantial Ġbarrier Ġto Ġincreasing Ġperformance Ġ( see ĠFigure ĠFIG REF 1 Ġ). </s> To Ġaddress Ġthis Ġneed Ġfor Ġdeveloping Ġmethods Ġof Ġtarget ted Ġinference , Ġthis Ġwork Ġmakes Ġthe Ġfollowing Ġcontributions : </s> Related Ġwork </s> Question Ġclassification Ġtypically Ġmakes Ġuse Ġof Ġa Ġcombination Ġof Ġsynt actic , Ġsemantic , Ġsurface , Ġand Ġembed ding Ġmethods . ĠSynt actic Ġpatterns ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġ, ĠB IB REF 21 Ġand Ġsynt actic Ġdependencies ĠB IB REF 3 Ġhave Ġbeen Ġshown Ġto Ġimprove Ġperformance , Ġwhile Ġsynt act ically Ġor Ġsem antically Ġimportant Ġwords Ġare Ġoften Ġexpanding Ġusing ĠWord net Ġhyper ny ms Ġor ĠUnified ĠMedical ĠLanguage ĠSystem Ġcategories Ġ( for Ġthe Ġmedical Ġdomain ) Ġto Ġhelp Ġmitigate Ġsp arsity ĠB IB REF 22 Ġ, ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġ. ĠKey word Ġidentification Ġhelps Ġidentify Ġspecific Ġterms Ġuseful Ġfor Ġclassification ĠB IB REF 25 Ġ, ĠB IB REF 3 Ġ, ĠB IB REF 26 Ġ. ĠSimilarly , Ġnamed Ġentity Ġrecogn izers ĠB IB REF 6 Ġ, ĠB IB REF 27 Ġor Ġlists Ġof Ġsem antically Ġrelated Ġwords ĠB IB REF 6 Ġ, ĠB IB REF 24 Ġcan Ġalso Ġbe Ġused Ġto Ġestablish Ġbroad Ġtopics Ġor Ġentity Ġcategories Ġand Ġmitigate Ġsp arsity , Ġas Ġcan Ġword Ġembed d ings ĠB IB REF 28 Ġ, ĠB IB REF 29 Ġ. ĠHere , Ġwe Ġempir ically Ġdemonstrate Ġmany Ġof Ġthese Ġexisting Ġmethods Ġdo Ġnot Ġtransfer Ġto Ġthe Ġscience Ġdomain . </s> The Ġhighest Ġperforming Ġquestion Ġclassification Ġsystems Ġtend Ġto Ġmake Ġuse Ġof Ġcustomized Ġrule - based Ġpattern Ġmatching ĠB IB REF 30 Ġ, ĠB IB REF 7 Ġ, Ġor Ġa Ġcombination Ġof Ġrule - based Ġand Ġmachine Ġlearning Ġapproaches ĠB IB REF 19 Ġ, Ġat Ġthe Ġexpense Ġof Ġincreased Ġmodel Ġconstruction Ġtime . ĠA Ġrecent Ġemphasis Ġon Ġlearned Ġmethods Ġhas Ġshown Ġa Ġlarge Ġset Ġof ĠCNN ĠB IB REF 29 Ġand ĠL ST M ĠB IB REF 8 Ġvariants Ġachieve Ġsimilar Ġaccuracy Ġon ĠT REC Ġquestion Ġclassification , Ġwith Ġthese Ġmodels Ġexhibiting Ġat Ġbest Ġsmall Ġgains Ġover Ġsimple Ġterm Ġfrequency Ġmodels . ĠThese Ġrecent Ġdevelopments Ġecho Ġthe Ġobservations Ġof ĠRoberts Ġet Ġal . ĠB IB REF 3 Ġ, Ġwho Ġshowed Ġthat Ġexisting Ġmethods Ġbeyond Ġterm Ġfrequency Ġmodels Ġfailed Ġto Ġgeneral ize Ġto Ġmedical Ġdomain Ġquestions . ĠHere Ġwe Ġshow Ġthat Ġstrong Ġperformance Ġacross Ġmultiple Ġdatasets Ġis Ġpossible Ġusing Ġa Ġsingle Ġlearned Ġmodel . </s> Due Ġto Ġthe Ġcost Ġinvolved Ġin Ġtheir Ġconstruction , Ġquestion Ġclassification Ġdatasets Ġand Ġclassification Ġtax onom ies Ġtend Ġto Ġbe Ġsmall , Ġwhich Ġcan Ġcreate Ġmethodological Ġchallenges . ĠRoberts Ġet Ġal . ĠB IB REF 3 Ġgenerated Ġthe Ġnext - largest Ġdataset Ġfrom ĠT REC , Ġcontaining Ġ2 , 9 36 Ġconsumer Ġhealth Ġquestions Ġclassified Ġinto Ġ13 Ġquestion Ġcategories . ĠMore Ġrecently , ĠWas im Ġet Ġal . ĠB IB REF 4 Ġgenerated Ġa Ġsmall Ġcorpus Ġof Ġ780 Ġbiomedical Ġdomain Ġquestions Ġorganized Ġinto Ġ88 Ġcategories . ĠIn Ġthe Ġeducation Ġdomain , ĠG ode a Ġet Ġal . ĠB IB REF 5 Ġcollected Ġa Ġset Ġof Ġ1 , 155 Ġclassroom Ġquestions Ġand Ġorganized Ġthese Ġinto Ġ16 Ġcategories . ĠTo Ġenable Ġa Ġdetailed Ġstudy Ġof Ġscience Ġdomain Ġquestion Ġclassification , Ġhere Ġwe Ġconstruct Ġa Ġlarge - scale Ġchallenge Ġdataset Ġthat Ġexceeds Ġthe Ġsize Ġand Ġclassification Ġspecificity Ġof Ġother Ġdatasets , Ġin Ġmany Ġcases Ġby Ġnearly Ġan Ġorder Ġof Ġmagnitude . </s> Questions Ġand ĠClassification ĠTax onomy </s> Questions : ĠWe Ġmake Ġuse Ġof Ġthe Ġ7 , 787 Ġscience Ġexam Ġquestions Ġof Ġthe ĠArist o ĠReason ing ĠChallenge Ġ( ARC ) Ġcorpus ĠB IB REF 31 Ġ, Ġwhich Ġcontains Ġstandardized Ġ3 rd Ġto Ġ9 th Ġgrade Ġscience Ġquestions Ġfrom Ġ12 ĠUS Ġstates Ġfrom Ġthe Ġpast Ġdecade . ĠEach Ġquestion Ġis Ġa Ġ4 - choice Ġmultiple Ġchoice Ġquestion . ĠSummary Ġstatistics Ġcomparing Ġthe Ġcomplexity Ġof ĠARC Ġand ĠT REC Ġquestions Ġare Ġshown Ġin ĠTable ĠT AB REF 5 Ġ. </s> Tax onomy : ĠStarting Ġwith Ġthe Ġsyll abus Ġfor Ġthe ĠNY ĠReg ents Ġexam , Ġwe Ġidentified Ġ9 Ġcoarse Ġquestion Ġcategories Ġ( Ast ron omy , ĠEarth ĠScience , ĠEnergy , ĠForces , ĠLife ĠScience , ĠMatter , ĠSafety , ĠScientific ĠMethod , ĠOther ), Ġthen Ġthrough Ġa Ġdata - driven Ġanalysis Ġof Ġ3 Ġexam Ġstudy Ġguides Ġand Ġthe Ġ3 , 370 Ġtraining Ġquestions , Ġexpanded Ġthe Ġtax onomy Ġto Ġinclude Ġ4 62 Ġfine - gr ained Ġcategories Ġacross Ġ6 Ġhierarchical Ġlevels Ġof Ġgran ularity . ĠThe Ġtax onomy Ġis Ġdesigned Ġto Ġallow Ġcategor izing Ġquestions Ġinto Ġbroad Ġcurriculum Ġtopics Ġat Ġit 's Ġco ars est Ġlevel , Ġwhile Ġlabels Ġat Ġfull Ġspecificity Ġseparate Ġquestions Ġinto Ġnarrow Ġproblem Ġdomains Ġsuitable Ġfor Ġtarget ted Ġinference Ġmethods . ĠBecause Ġof Ġits Ġsize , Ġa Ġsubset Ġof Ġthe Ġclassification Ġtax onomy Ġis Ġshown Ġin ĠTable ĠT AB REF 6 Ġ, Ġwith Ġthe Ġfull Ġtax onomy Ġand Ġclass Ġdefinitions Ġincluded Ġin Ġthe Ġsupplementary Ġmaterial . </s> An notation : ĠBecause Ġof Ġthe Ġcomplexity Ġof Ġthe Ġquestions , Ġit Ġis Ġpossible Ġfor Ġone Ġquestion Ġto Ġbridge Ġmultiple Ġcategories ĠâĢĵ Ġfor Ġexample , Ġa Ġwind Ġpower Ġgeneration Ġquestion Ġmay Ġspan Ġboth Ġrenewable Ġenergy Ġand Ġenergy Ġconversion . ĠWe Ġallow Ġup Ġto Ġ2 Ġlabels Ġper Ġquestion , Ġand Ġfound Ġthat Ġ16 % Ġof Ġquestions Ġrequired Ġmultiple Ġlabels . ĠEach Ġquestion Ġwas Ġindependently Ġannot ated Ġby Ġtwo Ġannot ators , Ġwith Ġthe Ġlead Ġannot ator Ġa Ġdomain Ġexpert Ġin Ġstandardized Ġexams . ĠAn not ators Ġfirst Ġindependently Ġannot ated Ġthe Ġentire Ġquestion Ġset , Ġthen Ġquestions Ġwithout Ġcomplete Ġagreement Ġwere Ġdiscussed Ġuntil Ġresolution . ĠBefore Ġresolution , Ġinter annot ator Ġagreement Ġ( Co hen 's ĠKappa ) Ġwas ĠIN LINE FORM 0 Ġ= Ġ0 . 58 Ġat Ġthe Ġfinest Ġlevel Ġof Ġgran ularity , Ġand ĠIN LINE FORM 1 Ġ= Ġ0 . 85 Ġwhen Ġconsidering Ġonly Ġthe Ġco ars est Ġ9 Ġcategories . ĠThis Ġis Ġconsidered Ġmoderate Ġto Ġstrong Ġagreement ĠB IB REF 32 Ġ. ĠBased Ġon Ġthe Ġresults Ġof Ġour Ġerror Ġanalysis Ġ( see ĠSection ĠSEC REF 21 Ġ), Ġwe Ġestimate Ġthe Ġoverall Ġaccuracy Ġof Ġthe Ġquestion Ġclassification Ġlabels Ġafter Ġresolution Ġto Ġbe Ġapproximately Ġ96 %. ĠWhile Ġthe Ġfull Ġtax onomy Ġcontains Ġ4 62 Ġfine - gr ained Ġcategories Ġderived Ġfrom Ġboth Ġstandardized Ġquestions , Ġstudy Ġguides , Ġand Ġexam Ġsyll abi , Ġwe Ġobserved Ġonly Ġ406 Ġof Ġthese Ġcategories Ġare Ġtested Ġin Ġthe ĠARC Ġquestion Ġset . </s> Question ĠClassification Ġon ĠScience ĠEx ams </s> We Ġidentified Ġ5 Ġcommon Ġmodels Ġin Ġprevious Ġwork Ġprimarily Ġintended Ġfor Ġlearned Ġclass ifiers Ġrather Ġthan Ġhand - crafted Ġrules . ĠWe Ġadapt Ġthese Ġmodels Ġto Ġa Ġmulti - label Ġhierarchical Ġclassification Ġtask Ġby Ġtraining Ġa Ġseries Ġof Ġone - vs - all Ġbinary Ġclass ifiers ĠB IB REF 34 Ġ, Ġone Ġfor Ġeach Ġlabel Ġin Ġthe Ġtax onomy . ĠWith Ġthe Ġexception Ġof Ġthe ĠCNN Ġand ĠB ERT Ġmodels , Ġfollowing Ġprevious Ġwork ĠB IB REF 19 Ġ, ĠB IB REF 3 Ġ, ĠB IB REF 8 Ġwe Ġmake Ġuse Ġof Ġan ĠS VM Ġclass ifier Ġusing Ġthe ĠLI BS v M Ġframework ĠB IB REF 35 Ġwith Ġa Ġlinear Ġkernel . ĠModels Ġare Ġtrained Ġand Ġevaluated Ġfrom Ġcoarse Ġto Ġfine Ġlevels Ġof Ġtax onomic Ġspecificity . ĠAt Ġeach Ġlevel Ġof Ġtax onomic Ġevaluation , Ġa Ġset Ġof Ġnon - over l apping Ġconfidence Ġscores Ġfor Ġeach Ġbinary Ġclass ifier Ġare Ġgenerated Ġand Ġsorted Ġto Ġproduce Ġa Ġlist Ġof Ġranked Ġlabel Ġpredictions . ĠWe Ġevaluate Ġthese Ġranks Ġusing ĠMean ĠAverage ĠPrecision ĠB IB REF 36 Ġ. ĠARC Ġquestions Ġare Ġevaluated Ġusing Ġthe Ġstandard Ġ3 , 370 Ġquestions Ġfor Ġtraining , Ġ8 69 Ġfor Ġdevelopment , Ġand Ġ3 , 548 Ġfor Ġtesting . </s> N - gram s , ĠPOS , ĠHier arch ical Ġfeatures : ĠA Ġbaseline Ġbag - of - words Ġmodel Ġincorporating Ġboth Ġtagged Ġand Ġunt agged Ġun ig rams Ġand Ġbig ams . ĠWe Ġalso Ġimplement Ġthe Ġhierarchical Ġclassification Ġfeature Ġof ĠLi Ġand ĠRoth ĠB IB REF 6 Ġ, Ġwhere Ġfor Ġa Ġgiven Ġquestion , Ġthe Ġoutput Ġof Ġthe Ġclass ifier Ġat Ġco arser Ġlevels Ġof Ġgran ularity Ġserves Ġas Ġinput Ġto Ġthe Ġclass ifier Ġat Ġthe Ġcurrent Ġlevel Ġof Ġgran ularity . </s> D epend encies : ĠBig rams Ġof ĠStanford Ġdependencies ĠB IB REF 37 Ġ. ĠFor Ġeach Ġword , Ġwe Ġcreate Ġone Ġunl abel ed Ġbig ram Ġfor Ġeach Ġoutgoing Ġlink Ġfrom Ġthat Ġword Ġto Ġit 's Ġdependency ĠB IB REF 20 Ġ, ĠB IB REF 3 Ġ. </s> Question ĠExpansion Ġwith ĠHyper ny ms : ĠWe Ġperform Ġhyper ny m Ġexpansion ĠB IB REF 22 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 3 Ġby Ġincluding ĠWord Net Ġhyper ny ms ĠB IB REF 38 Ġfor Ġthe Ġroot Ġdependency Ġword , Ġand Ġwords Ġon Ġit 's Ġdirect Ġoutgoing Ġlinks . ĠWord Net Ġsense Ġis Ġidentified Ġusing ĠLes k Ġword - sense Ġdis amb ig uation ĠB IB REF 39 Ġ, Ġusing Ġquestion Ġtext Ġfor Ġcontext . ĠWe Ġimplement Ġthe Ġhe uristic Ġof ĠVan - tu Ġet Ġal . ĠB IB REF 24 Ġ, Ġwhere Ġmore Ġdistant Ġhyper ny ms Ġreceive Ġless Ġweight . </s> Ess ential ĠTerms : ĠThough Ġnot Ġpreviously Ġreported Ġfor ĠQC , Ġwe Ġmake Ġuse Ġof Ġun ig rams Ġof Ġkeywords Ġextracted Ġusing Ġthe ĠScience ĠExam ĠEssential ĠTerm ĠExtract or Ġof ĠKh ash abi Ġet Ġal . ĠB IB REF 26 Ġ. ĠFor Ġeach Ġkeyword , Ġwe Ġcreate Ġone Ġbinary Ġun ig ram Ġfeature . </s> CNN : ĠKim ĠB IB REF 28 Ġdemonstrated Ġnear Ġstate - of - the - art Ġperformance Ġon Ġa Ġnumber Ġof Ġsentence Ġclassification Ġtasks Ġ( including ĠT REC Ġquestion Ġclassification ) Ġby Ġusing Ġpre - trained Ġword Ġembed d ings ĠB IB REF 40 Ġas Ġfeature Ġextract ors Ġin Ġa ĠCNN Ġmodel . ĠLei Ġet Ġal . ĠB IB REF 29 Ġshowed Ġthat Ġ10 ĠCNN Ġvariants Ġperform Ġwithin Ġ+/- 2 % Ġof ĠKim 's ĠB IB REF 28 Ġmodel Ġon ĠT REC ĠQC . ĠWe Ġreport Ġperformance Ġof Ġour Ġbest ĠCNN Ġmodel Ġbased Ġon Ġthe ĠMP - CNN Ġarchitecture Ġof ĠRao Ġet Ġal . ĠB IB REF 41 Ġ, Ġwhich Ġworks Ġto Ġestablish Ġthe Ġsimilarity Ġbetween Ġquestion Ġtext Ġand Ġthe Ġdefinition Ġtext Ġof Ġthe Ġquestion Ġclasses . ĠWe Ġadapt Ġthe ĠMP - CNN Ġmodel , Ġwhich Ġuses Ġa ĠâĢ ľ Si ames e âĢ Ŀ Ġstructure ĠB IB REF 33 Ġ, Ġto Ġcreate Ġseparate Ġrepresentations Ġfor Ġboth Ġthe Ġquestion Ġand Ġthe Ġquestion Ġclass . ĠThe Ġmodel Ġthen Ġmakes Ġuse Ġof Ġa Ġtriple Ġranking Ġloss Ġfunction Ġto Ġminimize Ġthe Ġdistance Ġbetween Ġthe Ġrepresentations Ġof Ġquestions Ġand Ġthe Ġcorrect Ġclass Ġwhile Ġsimultaneously Ġmaxim ising Ġthe Ġdistance Ġbetween Ġquestions Ġand Ġincorrect Ġclasses . ĠWe Ġoptimize Ġthe Ġnetwork Ġusing Ġthe Ġmethod Ġof ĠTu ĠB IB REF 42 Ġ. </s> BER T - Q C Ġ( This Ġwork ): ĠWe Ġmake Ġuse Ġof ĠB ERT ĠB IB REF 43 Ġ, Ġa Ġlanguage Ġmodel Ġusing Ġbid irection al Ġenc oder Ġrepresentations Ġfrom Ġtransform ers , Ġin Ġa Ġsentence - class ification Ġconfiguration . ĠAs Ġthe Ġoriginal Ġsettings Ġof ĠB ERT Ġdo Ġnot Ġsupport Ġmulti - label Ġclassification Ġscenarios , Ġand Ġtraining Ġa Ġseries Ġof Ġ406 Ġbinary Ġclass ifiers Ġwould Ġbe Ġcomput ationally Ġexpensive , Ġwe Ġuse Ġthe Ġduplication Ġmethod Ġof ĠTs ou m ak as Ġet Ġal . ĠB IB REF 34 Ġwhere Ġwe Ġenumer ate Ġmulti - label Ġquestions Ġas Ġmultiple Ġsingle - label Ġinstances Ġduring Ġtraining Ġby Ġdupl icating Ġquestion Ġtext , Ġand Ġassigning Ġeach Ġinstance Ġone Ġof Ġthe Ġmultiple Ġlabels . ĠEvaluation Ġfollows Ġthe Ġstandard Ġprocedure Ġwhere Ġwe Ġgenerate Ġa Ġlist Ġof Ġranked Ġclass Ġpredictions Ġbased Ġon Ġclass Ġprobabilities , Ġand Ġuse Ġthis Ġto Ġcalculate ĠMean ĠAverage ĠPrecision Ġ( MAP ) Ġand ĠPrecision @ 1 Ġ( P @ 1 ). ĠAs Ġshown Ġin ĠTable ĠT AB REF 7 Ġ, Ġthis ĠB ERT - Q C Ġmodel Ġachieves Ġour Ġbest Ġquestion Ġclassification Ġperformance , Ġsignificantly Ġexceeding Ġbaseline Ġperformance Ġon ĠARC Ġby Ġ0 . 12 ĠMAP Ġand Ġ13 . 5 % ĠP @ 1 . </s> Compar ison Ġwith ĠBench mark ĠDat as ets </s> Apart Ġfrom Ġterm Ġfrequency Ġmethods , Ġquestion Ġclassification Ġmethods Ġdeveloped Ġon Ġone Ġdataset Ġgenerally Ġdo Ġnot Ġexhibit Ġstrong Ġtransfer Ġperformance Ġto Ġother Ġdatasets ĠB IB REF 3 Ġ. ĠWhile ĠB ERT - Q C Ġachieves Ġlarge Ġgains Ġover Ġexisting Ġmethods Ġon Ġthe ĠARC Ġdataset , Ġhere Ġwe Ġdemonstrate Ġthat ĠB ERT - Q C Ġalso Ġmatches Ġstate - of - the - art Ġperformance Ġon ĠT REC ĠB IB REF 6 Ġ, Ġwhile Ġsurpass ing Ġstate - of - the - art Ġperformance Ġon Ġthe ĠG ARD Ġcorpus Ġof Ġconsumer Ġhealth Ġquestions ĠB IB REF 3 Ġand ĠMLB io Med L AT Ġcorpus Ġof Ġbiomedical Ġquestions ĠB IB REF 4 Ġ. ĠAs Ġsuch , ĠB ERT - Q C Ġis Ġthe Ġfirst Ġmodel Ġto Ġachieve Ġstrong Ġperformance Ġacross Ġmore Ġthan Ġone Ġquestion Ġclassification Ġdataset . </s> T REC Ġquestion Ġclassification Ġis Ġdivided Ġinto Ġseparate Ġcoarse Ġand Ġfine - gr ained Ġtasks Ġcentered Ġaround Ġinfer ring Ġthe Ġexpected Ġanswer Ġtypes Ġof Ġshort Ġopen - domain Ġfact oid Ġquestions . ĠT REC - 6 Ġincludes Ġ6 Ġcoarse Ġquestion Ġclasses Ġ( ab bre viation , Ġentity , Ġdescription , Ġhuman , Ġlocation , Ġnumeric ), Ġwhile ĠT REC - 50 Ġexpands Ġthese Ġinto Ġ50 Ġmore Ġfine - gr ained Ġtypes . ĠT REC Ġquestion Ġclassification Ġmethods Ġcan Ġbe Ġdivided Ġinto Ġthose Ġthat Ġlearn Ġthe Ġquestion Ġclassification Ġtask , Ġand Ġthose Ġthat Ġmake Ġuse Ġof Ġeither Ġhand - crafted Ġor Ġsemi - aut om ated Ġsynt actic Ġor Ġsemantic Ġextraction Ġrules Ġto Ġinfer Ġquestion Ġclasses . ĠTo Ġdate , Ġthe Ġbest Ġreported Ġaccuracy Ġfor Ġlearned Ġmethods Ġis Ġ98 . 0 % Ġby ĠXia Ġet Ġal . ĠB IB REF 8 Ġfor ĠT REC - 6 , Ġand Ġ91 . 6 % Ġby ĠVan - tu Ġet Ġal . ĠB IB REF 24 Ġfor ĠT REC - 50 . ĠMad ab ushi Ġet Ġal . ĠB IB REF 7 Ġachieve Ġthe Ġhighest Ġto - date Ġperformance Ġon ĠT REC - 50 Ġat Ġ97 . 2 %, Ġusing Ġrules Ġthat Ġleverage Ġthe Ġstrong Ġsynt actic Ġregular ities Ġin Ġthe Ġshort ĠT REC Ġfact oid Ġquestions . </s> We Ġcompare Ġthe Ġperformance Ġof ĠB ERT - Q C Ġwith Ġrecently Ġreported Ġperformance Ġon Ġthis Ġdataset Ġin ĠTable ĠT AB REF 11 Ġ. ĠB ERT - Q C Ġachieves Ġstate - of - the - art Ġperformance Ġon Ġfine - gr ained Ġclassification Ġ( T REC - 50 ) Ġfor Ġa Ġlearned Ġmodel Ġat Ġ92 . 0 % Ġaccuracy , Ġand Ġnear Ġstate - of - the - art Ġperformance Ġon Ġcoarse Ġclassification Ġ( T REC - 6 ) Ġat Ġ96 . 2 % Ġaccuracy . </s> Because Ġof Ġthe Ġchallenges Ġwith Ġcollecting Ġbiomedical Ġquestions , Ġthe Ġdatasets Ġand Ġclassification Ġtax onom ies Ġtend Ġto Ġbe Ġsmall , Ġand Ġrule - based Ġmethods Ġoften Ġachieve Ġstrong Ġresults ĠB IB REF 45 Ġ. ĠRoberts Ġet Ġal . ĠB IB REF 3 Ġcreated Ġthe Ġlargest Ġbiomedical Ġquestion Ġclassification Ġdataset Ġto Ġdate , Ġannot ating Ġ2 , 9 37 Ġconsumer Ġhealth Ġquestions Ġdrawn Ġfrom Ġthe ĠGenetic Ġand ĠRare ĠDiseases Ġ( G ARD ) Ġquestion Ġdatabase Ġwith Ġ13 Ġquestion Ġtypes , Ġsuch Ġas Ġanatomy , Ġdisease Ġcause , Ġdiagnosis , Ġdisease Ġmanagement , Ġand Ġprog n oses . ĠRoberts Ġet Ġal . ĠB IB REF 3 Ġfound Ġthese Ġquestions Ġlargely Ġresistant Ġto Ġlearning - based Ġmethods Ġdeveloped Ġfor ĠT REC Ġquestions . ĠTheir Ġbest Ġmodel Ġ( CP T 2 ), Ġshown Ġin ĠTable ĠT AB REF 17 Ġ, Ġmakes Ġuse Ġof Ġstemming Ġand Ġlists Ġof Ġsem antically Ġrelated Ġwords Ġand Ġcue Ġphrases Ġto Ġachieve Ġ80 . 4 % Ġaccuracy . ĠB ERT - Q C Ġreaches Ġ84 . 9 % Ġaccuracy Ġon Ġthis Ġdataset , Ġan Ġincrease Ġof Ġ+ 4 . 5 % Ġover Ġthe Ġbest Ġprevious Ġmodel . ĠWe Ġalso Ġcompare Ġperformance Ġon Ġthe Ġrecently Ġreleased ĠMLB io Med L AT Ġdataset ĠB IB REF 4 Ġ, Ġa Ġmulti - label Ġbiomedical Ġquestion Ġclassification Ġdataset Ġwith Ġ780 Ġquestions Ġlabeled Ġusing Ġ88 Ġclassification Ġtypes Ġdrawn Ġfrom Ġ133 ĠUnified ĠMedical ĠLanguage ĠSystem Ġ( U ML S ) Ġcategories . ĠTable ĠT AB REF 18 Ġshows ĠB ERT - Q C Ġexceeds Ġtheir Ġbest Ġmodel , Ġfocus - driven Ġsemantic Ġfeatures Ġ( F DS F ), Ġby Ġ+ 0 . 05 ĠMicro - F 1 Ġand Ġ+ 3 % Ġaccuracy . </s> Error ĠAnalysis </s> We Ġperformed Ġan Ġerror Ġanalysis Ġon Ġ50 ĠARC Ġquestions Ġwhere Ġthe ĠB ERT - Q C Ġsystem Ġdid Ġnot Ġpredict Ġthe Ġcorrect Ġlabel , Ġwith Ġa Ġsummary Ġof Ġmajor Ġerror Ġcategories Ġlisted Ġin ĠTable ĠT AB REF 20 Ġ. </s> Ass oci ative ĠErrors : ĠIn Ġ35 % Ġof Ġcases , Ġpredicted Ġlabels Ġwere Ġnearly Ġcorrect , Ġdiffering Ġfrom Ġthe Ġcorrect Ġlabel Ġonly Ġby Ġthe Ġfinest - gr ained Ġ( leaf ) Ġelement Ġof Ġthe Ġhierarchical Ġlabel Ġ( for Ġexample , Ġpredicting ĠMatter ĠIN LINE FORM 0 ĠChanges Ġof ĠState ĠIN LINE FORM 1 ĠBo iling Ġinstead Ġof ĠMatter ĠIN LINE FORM 2 ĠChanges Ġof ĠState ĠIN LINE FORM 3 ĠFree zing ). ĠThe Ġbulk Ġof Ġthe Ġremaining Ġerrors Ġwere Ġdue Ġto Ġquestions Ġcontaining Ġhighly Ġcorrelated Ġwords Ġwith Ġa Ġdifferent Ġclass , Ġor Ġclasses Ġthemselves Ġbeing Ġhighly Ġcorrelated . ĠFor Ġexample , Ġa Ġspecific Ġquestion Ġabout ĠWeather ĠModels Ġdiscusses ĠâĢ ľ en vironments âĢ Ŀ Ġchanging Ġover ĠâĢ ľ mill ions Ġof Ġyears âĢ Ŀ , Ġwhere Ġdiscussions Ġof Ġenvironments Ġand Ġlong Ġtime Ġperiods Ġtend Ġto Ġbe Ġassociated Ġwith Ġquestions Ġabout ĠLocations Ġof ĠFoss ils . ĠSimilarly , Ġa Ġquestion Ġcontaining Ġthe Ġword ĠâĢ ľ ev ap oration âĢ Ŀ Ġcould Ġbe Ġprimarily Ġfocused Ġon Ġeither ĠChanges Ġof ĠState Ġor Ġthe ĠWater ĠCycle Ġ( cloud Ġgeneration ), Ġand Ġmust Ġrely Ġon Ġknowledge Ġfrom Ġthe Ġentire Ġquestion Ġtext Ġto Ġdetermine Ġthe Ġcorrect Ġproblem Ġdomain . ĠWe Ġbelieve Ġthese Ġassoci ative Ġerrors Ġare Ġaddress able Ġtechnical Ġchallenges Ġthat Ġcould Ġultimately Ġlead Ġto Ġincreased Ġperformance Ġin Ġsubsequent Ġmodels . </s> Er rors Ġspecific Ġto Ġthe Ġmultiple - choice Ġdomain : ĠWe Ġobserved Ġthat Ġusing Ġboth Ġquestion Ġand Ġall Ġmultiple Ġchoice Ġanswer Ġtext Ġproduced Ġlarge Ġgains Ġin Ġquestion Ġclassification Ġperformance ĠâĢĵ Ġfor Ġexample , ĠB ERT - Q C Ġperformance Ġincreases Ġfrom Ġ0 . 516 Ġ( question Ġonly ) Ġto Ġ0 . 654 Ġ( question Ġand Ġall Ġfour Ġanswer Ġcandidates ), Ġan Ġincrease Ġof Ġ0 . 138 ĠMAP . ĠOur Ġerror Ġanalysis Ġobserved Ġthat Ġwhile Ġthis Ġsubstantially Ġincreases ĠQC Ġperformance , Ġit Ġchanges Ġthe Ġdistribution Ġof Ġerrors Ġmade Ġby Ġthe Ġsystem . ĠSpecifically , Ġ25 % Ġof Ġerrors Ġbecome Ġhighly Ġcorrelated Ġwith Ġan Ġincorrect Ġanswer Ġcandidate , Ġwhich Ġ( we Ġshow Ġin ĠSection ĠSEC REF 5 Ġ) Ġcan Ġreduce Ġthe Ġperformance Ġof ĠQ A Ġsol vers . </s> Question ĠAn sw ering Ġwith ĠQC ĠLab els </s> Because Ġof Ġthe Ġchallenges Ġof Ġerror ful Ġlabel Ġpredictions Ġcorrel ating Ġwith Ġincorrect Ġanswers , Ġit Ġis Ġdifficult Ġto Ġdetermine Ġthe Ġultimate Ġbenefit Ġa ĠQ A Ġmodel Ġmight Ġreceive Ġfrom Ġreporting ĠQC Ġperformance Ġin Ġisolation . ĠCou pling ĠQ A Ġand ĠQC Ġsystems Ġcan Ġoften Ġbe Ġlabor ious ĠâĢĵ Ġeither Ġa Ġlarge Ġnumber Ġof Ġindependent Ġsol vers Ġtargeted Ġto Ġspecific Ġquestion Ġtypes Ġmust Ġbe Ġconstructed ĠB IB REF 46 Ġ, Ġor Ġan Ġexisting Ġsingle Ġmodel Ġmust Ġbe Ġable Ġto Ġproduct ively Ġincorporate Ġquestion Ġclassification Ġinformation . ĠHere Ġwe Ġdem ost rate Ġthe Ġlatter ĠâĢĵ Ġthat Ġa ĠB ERT ĠQ A Ġmodel Ġis Ġable Ġto Ġincorporate Ġquestion Ġclassification Ġinformation Ġthrough Ġquery Ġexpansion . ĠB ERT ĠB IB REF 43 Ġrecently Ġdemonstrated Ġstate - of - the - art Ġperformance Ġon Ġbenchmark Ġquestion Ġanswering Ġdatasets Ġsuch Ġas ĠSQU a D ĠB IB REF 47 Ġ, Ġand Ġnear Ġhuman - level Ġperformance Ġon ĠSW AG ĠB IB REF 48 Ġ. ĠSimilarly , ĠPan Ġet Ġal . ĠB IB REF 49 Ġdemonstrated Ġthat ĠB ERT Ġachieves Ġthe Ġhighest Ġaccuracy Ġon Ġthe Ġmost Ġchallenging Ġsubset Ġof ĠARC Ġscience Ġquestions . ĠWe Ġmake Ġuse Ġof Ġa ĠB ERT ĠQ A Ġmodel Ġusing Ġthe Ġsame ĠQ A Ġparadigm Ġdescribed Ġby ĠPan Ġet Ġal . ĠB IB REF 49 Ġ, Ġwhere ĠQ A Ġis Ġmodeled Ġas Ġa Ġnext - sent ence Ġprediction Ġtask Ġthat Ġpredicts Ġthe Ġlikelihood Ġof Ġa Ġgiven Ġmultiple Ġchoice Ġanswer Ġcandidate Ġfollowing Ġthe Ġquestion Ġtext . ĠWe Ġevaluate Ġthe Ġquestion Ġtext Ġand Ġthe Ġtext Ġof Ġeach Ġmultiple Ġchoice Ġanswer Ġcandidate Ġseparately , Ġwhere Ġthe Ġanswer Ġcandidate Ġwith Ġthe Ġhighest Ġprob abl ity Ġis Ġselected Ġas Ġthe Ġpredicted Ġanswer Ġfor Ġa Ġgiven Ġquestion . ĠPerformance Ġis Ġevaluated Ġusing ĠPrecision @ 1 ĠB IB REF 36 Ġ. ĠAdditional Ġmodel Ġdetails Ġand Ġhyper param eters Ġare Ġincluded Ġin Ġthe ĠAppendix . </s> We Ġincorporate ĠQC Ġinformation Ġinto Ġthe ĠQ A Ġprocess Ġby Ġimplementing Ġa Ġvariant Ġof Ġa Ġquery Ġexpansion Ġmodel ĠB IB REF 50 Ġ. ĠSpecifically , Ġfor Ġa Ġgiven Ġ{ question , ĠQC _ label } Ġpair , Ġwe Ġexpand Ġthe Ġquestion Ġtext Ġby Ġconc aten ating Ġthe Ġdefinition Ġtext Ġof Ġthe Ġquestion Ġclassification Ġlabel Ġto Ġthe Ġstart Ġof Ġthe Ġquestion . ĠWe Ġuse Ġof Ġthe Ġtop Ġpredicted Ġquestion Ġclassification Ġlabel Ġfor Ġeach Ġquestion . ĠBecause ĠQC Ġlabels Ġare Ġhierarchical , Ġwe Ġappend Ġthe Ġlabel Ġdefinition Ġtext Ġfor Ġeach Ġlevel Ġof Ġthe Ġlabel ĠIN LINE FORM 0 Ġ. ĠAn Ġexam pe Ġof Ġthis Ġprocess Ġis Ġshown Ġin ĠTable ĠT AB REF 23 Ġ. </s> Figure ĠFIG REF 24 Ġshows ĠQ A Ġpe formance Ġusing Ġpredicted Ġlabels Ġfrom Ġthe ĠB ERT - Q C Ġmodel , Ġcompared Ġto Ġa Ġbaseline Ġmodel Ġthat Ġdoes Ġnot Ġcontain Ġquestion Ġclassification Ġinformation . ĠAs Ġpredicted Ġby Ġthe Ġerror Ġanalysis , Ġwhile Ġa Ġmodel Ġtrained Ġwith Ġquestion Ġand Ġanswer Ġcandidate Ġtext Ġperforms Ġbetter Ġat ĠQC Ġthan Ġa Ġmodel Ġusing Ġquestion Ġtext Ġalone , Ġa Ġlarge Ġproportion Ġof Ġthe Ġincorrect Ġpredictions Ġbecome Ġassociated Ġwith Ġa Ġnegative Ġanswer Ġcandidate , Ġreducing Ġoverall ĠQ A Ġperformance , Ġand Ġhighlighting Ġthe Ġimportance Ġof Ġevaluating ĠQC Ġand ĠQ A Ġmodels Ġtogether . ĠWhen Ġusing ĠB ERT - Q C Ġtrained Ġon Ġquestion Ġtext Ġalone , Ġat Ġthe Ġfinest Ġlevel Ġof Ġspecificity Ġ( L 6 ) Ġwhere Ġoverall Ġquestion Ġclassification Ġaccuracy Ġis Ġ57 . 8 % ĠP @ 1 , Ġquestion Ġclassification Ġsignificantly Ġimproves ĠQ A Ġperformance Ġby Ġ+ 1 . 7 % ĠP @ 1 ĠIN LINE FORM 0 Ġ. ĠUsing Ġgold Ġlabels Ġshows Ġceiling ĠQ A Ġperformance Ġcan Ġreach Ġ+ 10 . 0 % ĠP @ 1 Ġover Ġbaseline , Ġdemonstrating Ġthat Ġas Ġquestion Ġclassification Ġmodel Ġperformance Ġimproves , Ġsubstantial Ġfuture Ġgains Ġare Ġpossible . ĠAn Ġanalysis Ġof Ġexpected Ġgains Ġfor Ġa Ġgiven Ġlevel Ġof ĠQC Ġperformance Ġis Ġincluded Ġin Ġthe ĠAppendix , Ġshowing Ġapproximately Ġlinear Ġgains Ġin ĠQ A Ġperformance Ġabove Ġbaseline Ġfor ĠQC Ġsystems Ġable Ġto Ġachieve Ġover Ġ40 % Ġclassification Ġaccuracy . ĠBelow Ġthis Ġlevel , Ġthe Ġdecreased Ġperformance Ġfrom Ġnoise Ġinduced Ġby Ġincorrect Ġlabels Ġsurpass es Ġgains Ġfrom Ġcorrect Ġlabels . </s> Hyper param eters : ĠPilot Ġexperiments Ġon Ġboth Ġpre - trained ĠB ERT - Base Ġand ĠB ERT - Large Ġcheckpoints Ġshowed Ġsimilar Ġperformance Ġbenefits Ġat Ġthe Ġfinest Ġlevels Ġof Ġquestion Ġclassification Ġgran ularity Ġ( L 6 ), Ġbut Ġthe ĠB ERT - Large Ġmodel Ġdemonstrated Ġhigher Ġoverall Ġbaseline Ġperformance , Ġand Ġlarger Ġincremental Ġbenefits Ġat Ġlower Ġlevels Ġof ĠQC Ġgran ularity , Ġso Ġwe Ġevaluated Ġusing Ġthat Ġmodel . ĠWe Ġlightly Ġtuned Ġhyper param eters Ġon Ġthe Ġdevelopment Ġset Ġsurrounding Ġthose Ġreported Ġby ĠDev lin Ġet Ġal . ĠB IB REF 43 Ġ, Ġand Ġultimately Ġsettled Ġon Ġparameters Ġsimilar Ġto Ġtheir Ġoriginal Ġwork , Ġtempered Ġby Ġtechnical Ġlimitations Ġin Ġrunning Ġthe ĠB ERT - Large Ġmodel Ġon Ġavailable Ġhardware : Ġmaximum Ġsequence Ġlength Ġ= Ġ128 , Ġbatch Ġsize Ġ= Ġ16 , Ġlearning Ġrate : Ġ1 e - 5 . ĠWe Ġreport Ġperformance Ġas Ġthe Ġaverage Ġof Ġ10 Ġruns Ġfor Ġeach Ġdat ap oint . ĠThe Ġnumber Ġof Ġepoch s Ġwere Ġtuned Ġon Ġeach Ġrun Ġon Ġthe Ġdevelopment Ġset Ġ( to Ġa Ġmaximum Ġof Ġ8 Ġepoch s ), Ġwhere Ġmost Ġmodels Ġconver ged Ġto Ġmaximum Ġperformance Ġwithin Ġ5 Ġepoch s . </s> Pre ference Ġfor Ġunc or related Ġerrors Ġin Ġmultiple Ġchoice Ġquestion Ġclassification : ĠWe Ġprimarily Ġreport ĠQ A Ġperformance Ġusing ĠB ERT - Q C Ġtrained Ġusing Ġtext Ġfrom Ġonly Ġthe Ġmultiple Ġchoice Ġquestions Ġand Ġnot Ġtheir Ġanswer Ġcandidates . ĠWhile Ġthis Ġmodel Ġachieved Ġlower Ġoverall ĠQC Ġperformance Ġcompared Ġto Ġthe Ġmodel Ġtrained Ġwith Ġboth Ġquestion Ġand Ġmultiple Ġchoice Ġanswer Ġcandidate Ġtext , Ġit Ġachieved Ġslightly Ġhigher Ġperformance Ġin Ġthe ĠQ A + Q C Ġsetting . ĠOur Ġerror Ġanalysis Ġin ĠSection ĠSEC REF 21 Ġshows Ġthat Ġthough Ġmodels Ġtrained Ġon Ġboth Ġquestion Ġand Ġanswer Ġtext Ġcan Ġachieve Ġhigher ĠQC Ġperformance , Ġwhen Ġthey Ġmake ĠQC Ġerrors , Ġthe Ġerrors Ġtend Ġto Ġbe Ġhighly Ġcorrelated Ġwith Ġan Ġincorrect Ġanswer Ġcandidate , Ġwhich Ġcan Ġsubstantially Ġreduce ĠQ A Ġperformance . ĠThis Ġis Ġan Ġimportant Ġresult Ġfor Ġquestion Ġclassification Ġin Ġthe Ġcontext Ġof Ġmultiple Ġchoice Ġexams . In Ġthe Ġcontext Ġof Ġmultiple Ġchoice Ġexams , Ġcorrelated Ġnoise Ġcan Ġsubstantially Ġreduce ĠQ A Ġperformance , Ġmeaning Ġthe Ġkinds Ġof Ġerrors Ġthat Ġa Ġmodel Ġmakes Ġare Ġimportant , Ġand Ġevaluating ĠQC Ġperformance Ġin Ġcontext Ġwith ĠQ A Ġmodels Ġthat Ġmake Ġuse Ġof Ġthose ĠQC Ġsystems Ġis Ġcritical . </s> Related Ġto Ġthis Ġresult , Ġwe Ġprovide Ġan Ġanalysis Ġof Ġthe Ġnoise Ġsensitivity Ġof Ġthe ĠQ A + Q C Ġmodel Ġfor Ġdifferent Ġlevels Ġof Ġquestion Ġclassification Ġprediction Ġaccuracy . ĠHere , Ġwe Ġpert urb Ġgold Ġquestion Ġlabels Ġby Ġrandomly Ġselecting Ġa Ġproportion Ġof Ġquestions Ġ( between Ġ5 % Ġand Ġ40 %) Ġand Ġrandomly Ġassigning Ġthat Ġquestion Ġa Ġdifferent Ġlabel . ĠFigure ĠFIG REF 36 Ġshows Ġthat Ġthis Ġunc or related Ġnoise Ġprovides Ġroughly Ġlinear Ġdecreases Ġin Ġperformance , Ġand Ġstill Ġshows Ġmoderate Ġgains Ġat Ġ60 % Ġaccuracy Ġ( 40 % Ġnoise ) Ġwith Ġunc or related Ġnoise . ĠThis Ġsuggests Ġthat Ġwhen Ġmaking Ġerrors , Ġmaking Ġrandom Ġerrors Ġ( that Ġare Ġnot Ġcorrelated Ġto Ġincorrect Ġmultiple Ġchoice Ġanswers ) Ġis Ġpreferential . </s> Training Ġwith Ġpredicted Ġlabels : ĠWe Ġobserved Ġsmall Ġgains Ġwhen Ġtraining Ġthe ĠB ERT - Q A Ġmodel Ġwith Ġpredicted ĠQC Ġlabels . ĠWe Ġgenerate Ġpredicted Ġlabels Ġfor Ġthe Ġtraining Ġset Ġusing Ġ5 - fold Ġcross valid ation Ġover Ġonly Ġthe Ġtraining Ġquestions . </s> Statistics : ĠWe Ġuse Ġnon - param etric Ġboot strap Ġres am pling Ġto Ġcompare Ġbaseline Ġ( no Ġlabel ) Ġand Ġexperimental Ġ( Q C Ġlabeled ) Ġruns Ġof Ġthe ĠQ A + Q C Ġexperiment . ĠBecause Ġthe ĠB ERT - Q A Ġmodel Ġproduces Ġdifferent Ġperformance Ġvalues Ġacross Ġsuccessive Ġruns , Ġwe Ġperform Ġ10 Ġruns Ġof Ġeach Ġcondition . ĠWe Ġthen Ġcompute Ġpair wise Ġp - values Ġfor Ġeach Ġof Ġthe Ġ10 Ġno Ġlabel Ġand ĠQC Ġlabeled Ġruns Ġ( gener ating Ġ100 Ġcomparisons ), Ġthen Ġuse ĠFisher 's Ġmethod Ġto Ġcombine Ġthese Ġinto Ġa Ġfinal Ġstatistic . </s> Question Ġclassification Ġpaired Ġwith Ġquestion Ġanswering Ġshows Ġstatistically Ġsignificant Ġgains Ġof Ġ+ 1 . 7 % ĠP @ 1 Ġat ĠL 6 Ġusing Ġpredicted Ġlabels , Ġand Ġa Ġceiling Ġgain Ġof Ġup Ġto Ġ+ 10 % ĠP @ 1 Ġusing Ġgold Ġlabels . ĠThe ĠQ A Ġperformance Ġgraph Ġin ĠFigure ĠFIG REF 24 Ġcontains Ġtwo Ġdeviations Ġfrom Ġthe Ġexpectation Ġof Ġlinear Ġgains Ġwith Ġincreasing Ġspecificity , Ġat ĠL 1 Ġand ĠL 3 . ĠRegion Ġat ĠIN LINE FORM 0 ĠOn Ġgold Ġlabels , ĠL 3 Ġprovides Ġsmall Ġgains Ġover ĠL 2 , Ġwhere Ġas ĠL 4 Ġprovides Ġlarge Ġgains Ġover ĠL 3 . ĠWe Ġhypothes ize Ġthat Ġthis Ġis Ġbecause Ġapproximately Ġ57 % Ġof Ġquestion Ġlabels Ġbelong Ġto Ġthe ĠEarth ĠScience Ġor ĠLife ĠScience Ġcategories Ġwhich Ġhave Ġmuch Ġmore Ġdepth Ġthan Ġbreadth Ġin Ġthe Ġstandardized Ġscience Ġcurriculum , Ġand Ġas Ġsuch Ġthese Ġcategories Ġare Ġprimarily Ġdifferentiated Ġfrom Ġbroad Ġtopics Ġinto Ġdetailed Ġproblem Ġtypes Ġat Ġlevels ĠL 4 Ġthrough ĠL 6 . ĠMost Ġother Ġcurriculum Ġcategories Ġhave Ġmore Ġbreadth Ġthan Ġdepth , Ġand Ġshow Ġstrong Ġ( but Ġnot Ġnecessarily Ġfull ) Ġdifferentiation Ġat ĠL 2 . ĠRegion Ġat ĠIN LINE FORM 1 ĠPred icted Ġperformance Ġat ĠL 1 Ġis Ġhigher Ġthan Ġgold Ġperformance Ġat ĠL 1 . ĠWe Ġhypothes ize Ġthis Ġis Ġbecause Ġwe Ġtrain Ġusing Ġpredicted Ġrather Ġthan Ġgold Ġlabels , Ġwhich Ġprovides Ġa Ġboost Ġin Ġperformance . ĠTraining Ġon Ġgold Ġlabels Ġand Ġtesting Ġon Ġpredicted Ġlabels Ġsubstantially Ġreduces Ġthe Ġdifference Ġbetween Ġgold Ġand Ġpredicted Ġperformance . </s> Though Ġinitial Ġraw Ġinter annot ator Ġagreement Ġwas Ġmeasured Ġat ĠIN LINE FORM 0 Ġ, Ġto Ġmaximize Ġthe Ġquality Ġof Ġthe Ġannotation Ġthe Ġannot ators Ġperformed Ġa Ġsecond Ġpass Ġwhere Ġall Ġdisagreements Ġwere Ġmanually Ġresolved . ĠTable ĠT AB REF 30 Ġshows Ġquestion Ġclassification Ġperformance Ġof Ġthe ĠB ERT - Q C Ġmodel Ġat Ġ57 . 8 % ĠP @ 1 , Ġmeaning Ġ42 . 2 % Ġof Ġthe Ġpredicted Ġlabels Ġwere Ġdifferent Ġthan Ġthe Ġgold Ġlabels . ĠThe Ġquestion Ġclassification Ġerror Ġanalysis Ġin ĠTable ĠT AB REF 20 Ġfound Ġthat Ġof Ġthese Ġ42 . 2 % Ġof Ġerror ful Ġpredictions , Ġ10 % Ġof Ġerrors Ġ( 4 . 2 % Ġof Ġtotal Ġlabels ) Ġwere Ġcaused Ġby Ġthe Ġgold Ġlabels Ġbeing Ġincorrect . ĠThis Ġallows Ġus Ġto Ġestimate Ġthat Ġthe Ġoverall Ġquality Ġof Ġthe Ġannotation Ġ( the Ġproportion Ġof Ġquestions Ġthat Ġhave Ġa Ġcorrect Ġhuman Ġauthored Ġlabel ) Ġis Ġapproximately Ġ96 %. </s> Autom ating ĠError ĠAn alyses Ġwith ĠQC </s> Detailed Ġerror Ġanalyses Ġfor Ġquestion Ġanswering Ġsystems Ġare Ġtypically Ġlabor Ġintensive , Ġoften Ġrequiring Ġhours Ġor Ġdays Ġto Ġperform Ġmanually . ĠAs Ġa Ġresult Ġerror Ġanalyses Ġare Ġtypically Ġcompleted Ġinf requently , Ġin Ġspite Ġof Ġtheir Ġutility Ġto Ġkey Ġdecisions Ġin Ġthe Ġal g ort ith m Ġor Ġknowledge Ġconstruction Ġprocess . ĠHere Ġwe Ġshow Ġhaving Ġaccess Ġto Ġdetailed Ġquestion Ġclassification Ġlabels Ġspecifying Ġfine - gr ained Ġproblem Ġdomains Ġprovides Ġa Ġmechanism Ġto Ġautomatically Ġgenerate Ġerror Ġanalyses Ġin Ġseconds Ġinstead Ġof Ġdays . </s> To Ġillustrate Ġthe Ġutility Ġof Ġthis Ġapproach , ĠTable ĠT AB REF 26 Ġshows Ġthe Ġperformance Ġof Ġthe ĠB ERT ĠQ A + Q C Ġmodel Ġbroken Ġdown Ġby Ġspecific Ġquestion Ġclasses . ĠThis Ġallows Ġautomatically Ġidentifying Ġa Ġgiven Ġmodel 's Ġstrengths ĠâĢĵ Ġfor Ġexample , Ġhere Ġquestions Ġabout ĠHuman ĠHealth , ĠMaterial ĠProperties , Ġand ĠEarth 's ĠInner ĠCore Ġare Ġwell Ġaddressed Ġby Ġthe ĠB ERT - Q A Ġmodel , Ġand Ġachieve Ġwell Ġabove Ġthe Ġaverage ĠQ A Ġperformance Ġof Ġ49 %. ĠSimilarly , Ġareas Ġof Ġdeficit Ġinclude ĠChanges Ġof ĠState , ĠReprodu ction , Ġand ĠFood ĠChain ĠProcess es Ġquestions , Ġwhich Ġsee Ġbelow - average ĠQ A Ġperformance . ĠThe Ġlowest Ġperforming Ġclass , ĠSafety ĠProcedures , Ġdemonstrates Ġthat Ġwhile Ġthis Ġmodel Ġhas Ġstrong Ġperformance Ġin Ġmany Ġareas Ġof Ġscientific Ġreasoning , Ġit Ġis Ġworse Ġthan Ġchance Ġat Ġanswering Ġquestions Ġabout Ġsafety , Ġand Ġwould Ġbe Ġinappropriate Ġto Ġdeploy Ġfor Ġsafety - critical Ġtasks . </s> While Ġthis Ġanalysis Ġis Ġshown Ġat Ġan Ġintermediate Ġ( L 2 ) Ġlevel Ġof Ġspecificity Ġfor Ġspace , Ġmore Ġdetailed Ġanalyses Ġare Ġpossible . ĠFor Ġexample , Ġoverall ĠQ A Ġperformance Ġon ĠScientific ĠIn ference Ġquestions Ġis Ġnear Ġaverage Ġ( 47 %), Ġbut Ġincreasing Ġgran ularity Ġto ĠL 3 Ġwe Ġobserve Ġthat Ġquestions Ġaddressing ĠExperiment ĠDesign Ġor ĠMaking ĠInf erences ĠâĢĵ Ġchallenging Ġquestions Ġeven Ġfor Ġhumans ĠâĢĵ Ġperform Ġpoorly Ġ( 33 % Ġand Ġ20 %) Ġwhen Ġanswered Ġby Ġthe ĠQ A Ġsystem . ĠThis Ġallows Ġa Ġsystem Ġdesigner Ġto Ġintellig ently Ġtarget Ġproblem - specific Ġknowledge Ġresources Ġand Ġinference Ġmethods Ġto Ġaddress Ġdeficits Ġin Ġspecific Ġareas . </s> Conclusion </s> Question Ġclassification Ġcan Ġenable Ġtarget ting Ġquestion Ġanswering Ġmodels , Ġbut Ġis Ġchallenging Ġto Ġimplement Ġwith Ġhigh Ġperformance Ġwithout Ġusing Ġrule - based Ġmethods . ĠIn Ġthis Ġwork Ġwe Ġgenerate Ġthe Ġmost Ġfine - gr ained Ġchallenge Ġdataset Ġfor Ġquestion Ġclassification , Ġusing Ġcomplex Ġand Ġsynt act ically Ġdiverse Ġquestions , Ġand Ġshow Ġgains Ġof Ġup Ġto Ġ12 % Ġare Ġpossible Ġwith Ġour Ġquestion Ġclassification Ġmodel Ġacross Ġdatasets Ġin Ġopen , Ġscience , Ġand Ġmedical Ġdomains . ĠThis Ġmodel Ġis Ġthe Ġfirst Ġdemonstration Ġof Ġa Ġquestion Ġclassification Ġmodel Ġachieving Ġstate - of - the - art Ġresults Ġacross Ġbenchmark Ġdatasets Ġin Ġopen , Ġscience , Ġand Ġmedical Ġdomains . ĠWe Ġfurther Ġdemonstrate Ġattending Ġto Ġquestion Ġtype Ġcan Ġsignificantly Ġimprove Ġquestion Ġanswering Ġperformance , Ġwith Ġlarge Ġgains Ġpossible Ġas Ġqu es ion Ġclassification Ġperformance Ġimproves . ĠOur Ġerror Ġanalysis Ġsuggests Ġthat Ġdeveloping Ġhigh - pre cision Ġmethods Ġof Ġquestion Ġclassification Ġindependent Ġof Ġtheir Ġrecall Ġcan Ġoffer Ġthe Ġopportunity Ġto Ġincrement ally Ġmake Ġuse Ġof Ġthe Ġbenefits Ġof Ġquestion Ġclassification Ġwithout Ġsuffering Ġthe Ġconsequences Ġof Ġclassification Ġerrors Ġon ĠQ A Ġperformance . </s> Resources </s> Our ĠAppendix Ġand Ġsupplementary Ġmaterial Ġ( available Ġat Ġhttp :// www . c ognitive ai . org / ex plan ation bank /) Ġincludes Ġdata , Ġcode , Ġexperiment Ġdetails , Ġand Ġnegative Ġresults . </s> Acknowled gements </s> The Ġauthors Ġwish Ġto Ġthank ĠElizabeth ĠW ain wright Ġand ĠStephen ĠMar mor stein Ġfor Ġpilot ing Ġan Ġearlier Ġversion Ġof Ġthe Ġquestion Ġclassification Ġannotation . ĠWe Ġthank Ġthe ĠAllen ĠIns itute Ġfor ĠArtificial ĠIntelligence Ġand ĠNational ĠScience ĠF oun ation Ġ( NS F Ġ18 159 48 Ġto ĠPJ ) Ġfor Ġfunding Ġthis Ġwork . </s> An notation </s> Class ification ĠTax onomy : ĠThe Ġfull Ġclassification Ġtax onomy Ġis Ġincluded Ġin Ġseparate Ġfiles , Ġboth Ġcoupled Ġwith Ġdefinitions , Ġand Ġas Ġa Ġgraphical Ġvisualization . </s> An notation ĠProcedure : ĠPrimary Ġannotation Ġtook Ġplace Ġover Ġapproximately Ġ8 Ġweeks . ĠAn not ators Ġwere Ġinstructed Ġto Ġprovide Ġup Ġto Ġ2 Ġlabels Ġfrom Ġthe Ġfull Ġclassification Ġtax onomy Ġ( 462 Ġlabels ) Ġthat Ġwere Ġappropriate Ġfor Ġeach Ġquestion , Ġand Ġto Ġprovide Ġthe Ġmost Ġspecific Ġlabel Ġavailable Ġin Ġthe Ġtax onomy Ġfor Ġa Ġgiven Ġquestion . ĠOf Ġthe Ġ4 62 Ġlabels Ġin Ġthe Ġclassification Ġtax onomy , Ġthe ĠARC Ġquestions Ġhad Ġnon - zero Ġcounts Ġin Ġ406 Ġquestion Ġtypes . ĠRare ly , Ġquestions Ġwere Ġencountered Ġby Ġannot ators Ġthat Ġdid Ġnot Ġclearly Ġfit Ġinto Ġa Ġlabel Ġat Ġthe Ġend Ġof Ġthe Ġtax onomy , Ġand Ġin Ġthese Ġcases Ġthe Ġannot ators Ġwere Ġinstructed Ġto Ġchoose Ġa Ġmore Ġgeneric Ġlabel Ġhigher Ġup Ġthe Ġtax onomy Ġthat Ġwas Ġappropriate . ĠThis Ġoccurred Ġwhen Ġthe Ġproduction Ġtax onomy Ġfailed Ġto Ġhave Ġspecific Ġcategories Ġfor Ġinf requent Ġquestions Ġtesting Ġknowledge Ġthat Ġis Ġnot Ġa Ġstandard Ġpart Ġof Ġthe Ġscience Ġcurriculum . ĠFor Ġexample , Ġthe Ġquestion : </s> Which Ġmaterial Ġis Ġthe Ġbest Ġnatural Ġresource Ġto Ġuse Ġfor Ġmaking Ġwater - resistant Ġshoes ? Ġ( A ) Ġcotton Ġ( B ) Ġleather Ġ( C ) Ġplastic Ġ( D ) Ġwool </s> tests Ġa Ġstudent 's Ġknowledge Ġof Ġthe Ġwater Ġresistance Ġof Ġdifferent Ġmaterials . ĠBecause Ġthis Ġis Ġnot Ġa Ġstandard Ġpart Ġof Ġthe Ġcurriculum , Ġand Ġwasn 't Ġidentified Ġas Ġa Ġcommon Ġtopic Ġin Ġthe Ġtraining Ġquestions , Ġthe Ġannot ators Ġtag Ġthis Ġquestion Ġas Ġbelonging Ġto ĠMatter ĠIN LINE FORM 0 ĠProperties Ġof ĠMaterials , Ġrather Ġthan Ġa Ġmore Ġspecific Ġcategory . </s> Questions Ġfrom Ġthe Ġtraining , Ġdevelopment , Ġand Ġtest Ġsets Ġwere Ġrandomly Ġshuff led Ġto Ġcounter balance Ġany Ġlearning Ġeffects Ġduring Ġthe Ġannotation Ġprocedure , Ġbut Ġwere Ġpresented Ġin Ġgrade Ġorder Ġ( 3 rd Ġto Ġ9 th Ġgrade ) Ġto Ġreduce Ġcontext Ġswitching Ġ( a Ġgiven Ġgrade Ġlevel Ġtends Ġto Ġuse Ġa Ġsimilar Ġsubset Ġof Ġthe Ġtax onomy ĠâĢĵ Ġfor Ġexample , Ġ3 rd Ġgrade Ġquestions Ġgenerally Ġdo Ġnot Ġaddress ĠChemical ĠEqu ations Ġor ĠNew tons Ġ1 st ĠLaw Ġof ĠMotion ). </s> Inter annot ator ĠAgreement : ĠTo Ġincrease Ġquality Ġand Ġconsistency , Ġeach Ġannot ator Ġannot ated Ġthe Ġentire Ġdataset Ġof Ġ7 , 787 Ġquestions . ĠTwo Ġannot ators Ġwere Ġused , Ġwith Ġthe Ġlead Ġannot ator Ġpossessing Ġprevious Ġprofessional Ġdomain Ġexpertise . ĠAn notation Ġproceeded Ġin Ġa Ġtwo - stage Ġprocess , Ġwhere Ġin Ġstage Ġ1 Ġannot ators Ġcompleted Ġtheir Ġannotation Ġindependently , Ġand Ġin Ġstage Ġ2 Ġeach Ġof Ġthe Ġquestions Ġwhere Ġthe Ġannot ators Ġdid Ġnot Ġhave Ġcomplete Ġagreement Ġwere Ġmanually Ġresolved Ġby Ġthe Ġannot ators , Ġresulting Ġin Ġhigh - quality Ġclassification Ġannotation . </s> Because Ġeach Ġquestion Ġcan Ġhave Ġup Ġto Ġtwo Ġlabels , Ġwe Ġtreat Ġeach Ġlabel Ġfor Ġa Ġgiven Ġquestion Ġas Ġa Ġseparate Ġevaluation Ġof Ġinter annot ator Ġagreement . ĠThat Ġis , Ġfor Ġquestions Ġwhere Ġboth Ġannot ators Ġlabeled Ġeach Ġquestion Ġas Ġhaving Ġ1 Ġor Ġ2 Ġlabels , Ġwe Ġtreat Ġthis Ġas Ġ1 Ġor Ġ2 Ġseparate Ġevaluations Ġof Ġinter annot ator Ġagreement . ĠFor Ġcases Ġwhere Ġone Ġannot ator Ġlabeled Ġas Ġquestion Ġas Ġhaving Ġ1 Ġlabel , Ġand Ġthe Ġother Ġannot ator Ġlabeled Ġthat Ġsame Ġquestion Ġas Ġhaving Ġ2 Ġlabels , Ġwe Ġconservative ly Ġtreat Ġthis Ġas Ġtwo Ġseparate Ġinter annot ator Ġagreements Ġwhere Ġone Ġannot ator Ġfailed Ġto Ġspecify Ġthe Ġsecond Ġlabel Ġand Ġhad Ġzero Ġagreement Ġon Ġthat Ġunspecified Ġlabel . </s> Though Ġthe Ġclassification Ġprocedure Ġwas Ġfine - gr ained Ġcompared Ġto Ġother Ġquestion Ġclassification Ġtax onom ies , Ġcontaining Ġan Ġunusually Ġlarge Ġnumber Ġof Ġclasses Ġ( 406 ), Ġoverall Ġraw Ġinter annot ator Ġagreement Ġbefore Ġresolution Ġwas Ġhigh Ġ( Co hen 's ĠIN LINE FORM 0 Ġ= Ġ0 . 58 ). ĠWhen Ġlabels Ġare Ġtrunc ated Ġto Ġa Ġmaximum Ġtax onomy Ġdepth Ġof ĠN , Ġraw Ġinter annot ator Ġincreases Ġto ĠIN LINE FORM 1 Ġ= Ġ0 . 85 Ġat Ġthe Ġco ars est Ġ( 9 Ġclass ) Ġlevel Ġ( see ĠTable ĠT AB REF 28 Ġ). ĠThis Ġis Ġconsidered Ġmoderate Ġto Ġstrong Ġagreement Ġ( see ĠMcH ugh ĠB IB REF 32 Ġfor Ġa Ġdiscussion Ġof Ġthe Ġinterpretation Ġof Ġthe ĠKappa Ġstatistic ). ĠBased Ġon Ġthe Ġresults Ġof Ġan Ġerror Ġanalysis Ġon Ġthe Ġquestion Ġclassification Ġsystem Ġ( see ĠSection ĠUID 38 Ġ), Ġwe Ġestimate Ġthat Ġthe Ġoverall Ġaccuracy Ġof Ġthe Ġquestion Ġclassification Ġlabels Ġafter Ġresolution Ġis Ġapproximately Ġ96 % Ġ. </s> An not ators Ġdisagreed Ġon Ġ3 441 Ġ( 44 . 2 %) Ġof Ġquestions . ĠPrimary Ġsources Ġof Ġdisagreement Ġbefore Ġresolution Ġincluded Ġeach Ġannot ator Ġchoosing Ġa Ġsingle Ġcategory Ġfor Ġquestions Ġrequiring Ġmultiple Ġlabels Ġ( e . g . Ġannot ator Ġ1 Ġassigning Ġa Ġlabel Ġof ĠX , Ġand Ġannot ator Ġ2 Ġassigning Ġa Ġlabel Ġof ĠY , Ġwhen Ġthe Ġgold Ġlabel Ġwas Ġmult il abel ĠX , ĠY ), Ġwhich Ġwas Ġobserved Ġin Ġ18 % Ġof Ġdisagreements . ĠSimilarly , Ġwe Ġobserved Ġannot ators Ġchoosing Ġsimilar Ġlabels Ġbut Ġat Ġdifferent Ġlevels Ġof Ġspecificity Ġin Ġthe Ġtax onomy Ġ( e . g . Ġannot ator Ġ1 Ġassigning Ġa Ġlabel Ġof ĠMatter ĠIN LINE FORM 0 ĠChanges Ġof ĠState ĠIN LINE FORM 1 ĠBo iling , Ġwhere Ġannot ator Ġ2 Ġassigned ĠMatter ĠIN LINE FORM 2 ĠChanges Ġof ĠState ), Ġwhich Ġoccurred Ġin Ġ12 % Ġof Ġdisagreements Ġbefore Ġresolution . </s> Question ĠClassification </s> Because Ġof Ġspace Ġlimitations Ġthe Ġquestion Ġclassification Ġresults Ġare Ġreported Ġin ĠTable ĠT AB REF 7 Ġonly Ġusing ĠMean ĠAverage ĠPrecision Ġ( MAP ). ĠWe Ġalso Ġinclude ĠPrecision @ 1 Ġ( P @ 1 ), Ġthe Ġoverall Ġaccuracy Ġof Ġthe Ġhighest - ranked Ġprediction Ġfor Ġeach Ġquestion Ġclassification Ġmodel , Ġin ĠTable ĠT AB REF 30 Ġ. </s> CNN : ĠWe Ġimplemented Ġthe ĠCNN Ġsentence Ġclass ifier Ġof ĠKim ĠB IB REF 28 Ġ, Ġwhich Ġdemonstrated Ġnear Ġstate - of - the - art Ġperformance Ġon Ġa Ġnumber Ġof Ġsentence Ġclassification Ġtasks Ġ( including ĠT REC Ġquestion Ġclassification ) Ġby Ġusing Ġpre - trained Ġword Ġembed d ings ĠB IB REF 40 Ġas Ġfeature Ġextract ors Ġin Ġa ĠCNN Ġmodel . ĠWe Ġadapted Ġthe Ġoriginal ĠCNN Ġnon - static Ġmodel Ġto Ġmulti - label Ġclassification Ġby Ġchanging Ġthe Ġfully Ġconnected Ġsoft max Ġlayer Ġto Ġs igm oid Ġlayer Ġto Ġproduce Ġa Ġs igm oid Ġoutput Ġfor Ġeach Ġlabel Ġsimultaneously . ĠWe Ġfollowed Ġthe Ġsame Ġparameter Ġsettings Ġreported Ġby ĠKim Ġet Ġal . Ġexcept Ġthe Ġlearning Ġrate , Ġwhich Ġwas Ġtuned Ġbased Ġon Ġthe Ġdevelopment Ġset . ĠPilot Ġexperiments Ġdid Ġnot Ġshow Ġa Ġperformance Ġimprovement Ġover Ġthe Ġbaseline Ġmodel . </s> Label ĠDefinitions : ĠQuestion Ġterms Ġcan Ġbe Ġmapped Ġto Ġcategories Ġusing Ġmanual Ġhe ur istics ĠB IB REF 19 Ġ. ĠTo Ġmitigate Ġsp arsity Ġand Ġlimit Ġhe uristic Ġuse , Ġhere Ġwe Ġgenerated Ġa Ġfeature Ġcomparing Ġthe Ġcos ine Ġsimilarity Ġof Ġcomposite Ġembed ding Ġvectors ĠB IB REF 51 Ġrepresenting Ġquestion Ġtext Ġand Ġcategory Ġdefinition Ġtext , Ġusing Ġpret rained ĠGl o Ve Ġembed d ings ĠB IB REF 52 Ġ. ĠPilot Ġexperiments Ġshowed Ġthat Ġperformance Ġdid Ġnot Ġsignificantly Ġimprove . </s> Question ĠExpansion Ġwith ĠHyper ny ms Ġ( Pro base ĠVersion ): ĠOne Ġof Ġthe Ġchallenges Ġof Ġhyper ny m Ġexpansion ĠB IB REF 22 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 3 Ġis Ġdetermining Ġa Ġhe uristic Ġfor Ġthe Ġtermination Ġdepth Ġof Ġhyper ny m Ġexpansion , Ġas Ġin ĠVan - tu Ġet Ġal . ĠB IB REF 24 Ġ. ĠBecause Ġscience Ġexam Ġquestions Ġare Ġoften Ġgrounded Ġin Ġspecific Ġexamples Ġ( e . g . Ġa Ġcar Ġrolling Ġdown Ġa Ġhill Ġcoming Ġto Ġa Ġstop Ġdue Ġto Ġfriction ), Ġwe Ġhypothesized Ġthat Ġknowing Ġcertain Ġcategories Ġof Ġentities Ġcan Ġbe Ġimportant Ġfor Ġidentifying Ġspecific Ġquestion Ġtypes ĠâĢĵ Ġfor Ġexample , Ġobserving Ġthat Ġa Ġquestion Ġcontains Ġa Ġkind Ġof Ġanimal Ġmay Ġbe Ġsuggestive Ġof Ġa ĠLife ĠScience Ġquestion , Ġwhere Ġsimilarly Ġvehicles Ġor Ġmaterials Ġpresent Ġin Ġquestions Ġmay Ġsuggest Ġquestions Ġabout ĠForces Ġor ĠMatter , Ġrespectively . ĠThe Ġchallenge Ġwith ĠWord Net Ġis Ġthat Ġkey Ġhyper ny ms Ġcan Ġbe Ġat Ġvery Ġdifferent Ġdepths Ġfrom Ġquery Ġterms ĠâĢĵ Ġfor Ġexample , ĠâĢ ľ cat âĢ Ŀ Ġis Ġdistance Ġ10 Ġaway Ġfrom Ġliving Ġthing , ĠâĢ ľ car âĢ Ŀ Ġis Ġdistance Ġ4 Ġaway Ġfrom Ġvehicle , Ġand ĠâĢ ľ co pper âĢ Ŀ Ġis Ġdistance Ġ2 Ġaway Ġfrom Ġmaterial . ĠCho osing Ġa Ġstatic Ġthreshold Ġ( or Ġdecaying Ġthreshold , Ġas Ġin ĠVan - tu Ġet Ġal . ĠB IB REF 24 Ġ) Ġwill Ġinher ient ly Ġreduce Ġrecall Ġand Ġlimit Ġthe Ġutility Ġof Ġthis Ġmethod Ġof Ġquery Ġexpansion . </s> To Ġaddress Ġthis , Ġwe Ġpil oted Ġa Ġhyper ny m Ġexpansion Ġexperiment Ġusing Ġthe ĠPro base Ġtax onomy ĠB IB REF 53 Ġ, Ġa Ġcollection Ġof Ġ20 . 7 M Ġis - a Ġpairs Ġmined Ġfrom Ġthe Ġweb , Ġin Ġplace Ġof ĠWord Net . ĠBecause Ġthe Ġtax onomic Ġpairs Ġin ĠPro base Ġcome Ġfrom Ġuse Ġin Ġnatural istic Ġsettings , Ġlinks Ġtend Ġto Ġjump Ġlevels Ġin Ġthe ĠWord Net Ġtax onomy Ġand Ġbe Ġexpressed Ġin Ġcommon Ġforms . ĠFor Ġexample , ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ, Ġand ĠIN LINE FORM 2 Ġ, Ġare Ġeach Ġdistance Ġ1 Ġin Ġthe ĠPro base Ġtax onomy , Ġand Ġhigh - frequency Ġ( i . e . Ġhigh - confidence ) Ġtax onomic Ġpairs . </s> Similar Ġto Ġquery Ġexpansion Ġusing ĠWord Net ĠHyper ny ms , Ġour Ġpilot Ġexperiments Ġdid Ġnot Ġobserve Ġa Ġbenefit Ġto Ġusing ĠPro base Ġhyper ny ms Ġover Ġthe Ġbaseline Ġmodel . ĠAn Ġerror Ġanalysis Ġsuggested Ġthat Ġthe Ġlarge Ġnumber Ġof Ġnoisy Ġand Ġout - of - context Ġlinks Ġpresent Ġin ĠPro base Ġmay Ġhave Ġreduced Ġperformance , Ġand Ġin Ġresponse Ġwe Ġconstructed Ġa Ġfiltered Ġlist Ġof Ġ7 10 Ġkey Ġhyper ny m Ġcategories Ġmanually Ġfiltered Ġfrom Ġa Ġlist Ġof Ġhyper ny ms Ġseeded Ġusing Ġhigh - frequency Ġwords Ġfrom Ġan Ġin - house Ġcorpus Ġof Ġ250 Ġin - domain Ġscience Ġtextbooks . ĠWe Ġalso Ġdid Ġnot Ġobserve Ġa Ġbenefit Ġto Ġquestion Ġclassification Ġover Ġthe Ġbaseline Ġmodel Ġwhen Ġexpanding Ġonly Ġto Ġthis Ġmanually Ġcurated Ġlist Ġof Ġkey Ġhyper ny ms . </s> Topic Ġwords : ĠWe Ġmade Ġuse Ġof Ġthe Ġ77 ĠT REC Ġword Ġlists Ġof ĠLi Ġand ĠRoth ĠB IB REF 6 Ġ, Ġcontaining Ġa Ġtotal Ġof Ġ3 , 257 Ġterms , Ġas Ġwell Ġas Ġan Ġin - house Ġset Ġof Ġ144 Ġword Ġlists Ġon Ġgeneral Ġand Ġelementary Ġscience Ġtopics Ġmined Ġfrom Ġthe Ġweb , Ġsuch Ġas ĠAN IM ALS , ĠV EG ET AB LES , Ġand ĠV EH IC LES , Ġcontaining Ġa Ġtotal Ġof Ġ29 , 059 Ġwords . ĠTo Ġmitigate Ġsp arsity , Ġfeatures Ġtake Ġthe Ġform Ġof Ġcounts Ġfor Ġa Ġspecific Ġtopic ĠâĢĵ Ġdetecting Ġthe Ġwords Ġturtle Ġand Ġgir affe Ġin Ġa Ġquestion Ġwould Ġprovide Ġa Ġcount Ġof Ġ2 Ġfor Ġthe ĠAN IM AL Ġfeature . ĠThis Ġprovides Ġa Ġlight Ġform Ġof Ġdomain - specific Ġentity Ġand Ġaction Ġ( e . g . Ġtypes Ġof Ġchanges ) Ġrecognition . ĠPilot Ġexperiments Ġshowed Ġthat Ġthis Ġword list Ġfeature Ġdid Ġadd Ġa Ġmodest Ġperformance Ġbenefit Ġof Ġapproximately Ġ2 % Ġto Ġquestion Ġclassification Ġaccuracy . ĠTaken Ġtogether Ġwith Ġour Ġresults Ġon Ġhyper ny m Ġexpansion , Ġthis Ġsuggests Ġthat Ġmanually Ġcurated Ġword lists Ġcan Ġshow Ġmodest Ġbenefits Ġfor Ġquestion Ġclassification Ġperformance , Ġbut Ġat Ġthe Ġexpense Ġof Ġsubstantial Ġeffort Ġin Ġauthor ing Ġor Ġcollecting Ġthese Ġextensive Ġword lists . </s> Hyper param eters : ĠFor Ġeach Ġlayer Ġof Ġthe Ġclass Ġlabel Ġhierarchy , Ġwe Ġtune Ġthe Ġhyper param eters Ġbased Ġon Ġthe Ġdevelopment Ġset . ĠWe Ġuse Ġthe Ġpret rained ĠB ERT - Base Ġ( unc ased ) Ġcheckpoint . ĠWe Ġuse Ġthe Ġfollowing Ġhyper param eters : Ġmaximum Ġsequence Ġlength Ġ= Ġ256 , Ġbatch Ġsize Ġ= Ġ16 , Ġlearning Ġrates : Ġ2 e - 5 Ġ( L 1 ), Ġ5 e - 5 Ġ( L 2 - L 6 ), Ġepoch s : Ġ5 Ġ( L 1 ), Ġ25 Ġ( L 2 - L 6 ). </s> Statistics : ĠWe Ġuse Ġnon - param etric Ġboot strap Ġres am pling Ġto Ġcompare Ġthe Ġbaseline Ġ( Li Ġand ĠRoth ĠB IB REF 6 Ġmodel ) Ġto Ġall Ġexperimental Ġmodels Ġto Ġdetermine Ġsignificance , Ġusing Ġ10 , 000 Ġboot strap Ġres amples .