 we use several of the MCQA baseline models first introduced in BIBREF0
EXTRACTIVE
Choice-Only model, which is a variant of the well-known hypothesis-only baseline, Choice-to-choice model, tries to single out a given answer choice relative to other choices, Question-to-choice model, in contrast, uses the contextual representations for each question and individual choice and an attention model Att model to get a score
EXTRACTIVE
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks. When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42). To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models.
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42). To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models. Following the notation from BIBREF0, for any given sequence $s$ of tokens in $\lbrace q^{(j)}, a_{1}^{(j)},...,a_{N}^{(j)}\rbrace $ in $D$, an encoding of $s$ is given as $h_{s}^{(j)} = \textbf {BiLSTM}(\textsc {embed}(s)) \in \mathbb {R}^{|s| \times 2h}$ (where $h$ is the dimension of the hidden state in each directional network, and embed$(\cdot )$ is an embedding function that assigns token-level embeddings to each token in $s$). A contextual representation for each $s$ is then built by applying an element-wise max operation over $h_{s}$ as follows: With these contextual representations, different baseline models can be constructed. For example, a Choice-Only model, which is a variant of the well-known hypothesis-only baseline used in NLI BIBREF46, scores each choice $c_{i}$ in the following way: for $\textbf {W}^{T} \in \mathbb {R}^{2h}$ independently of the question and assigns a probability to each answer $p_{i}^{(j)} \propto e^{\alpha _{i}^{(j)}}$. A slight variant of this model, the Choice-to-choice model, tries to single out a given answer choice relative to other choices by scoring all choice pairs $\alpha _{i,i^{\prime }}^{(j)} = \textsc {Att}(r^{(j)}_{c_{i}},r^{(j)}_{c_{i^{\prime }}}) \in \mathbb {R}$ using a learned attention mechanism Att and finding the choice with the minimal similarity to other options (for full details, see their original paper). In using these partial-input baselines, which we train directly on each target probe, we can check whether systematic biases related to answer choices were introduced into the data creation process. A Question-to-choice model, in contrast, uses the contextual representations for each question and individual choice and an attention model Att model to get a score $\alpha ^{(j)}_{q,i} = \textsc {Att}(r^{(j)}_{q},r^{(j)}_{c_{i}}) \in \mathbb {R}$ as above. Here we also experiment with using ESIM BIBREF47 to generate the contextual representations $r$, as well as a simpler VecSimilarity model that measures the average vector similarity between question and answer tokens: $\alpha ^{(j)}_{q,i} = \textsc {Sim}(\textsc {embed}(q^{(j)}),\textsc {embed}(c^{(j)}_{i}))$. In contrast to the models above, these sets of baselines are used to check for artifacts between questions and answers that are not captured in the partial-input baselines (see discussion in BIBREF49) and ensure that the overall MCQA tasks are sufficiently difficult for our transformer models.
1912.13337
Introduction </s> Autom atically Ġanswering Ġquestions , Ġespecially Ġin Ġthe Ġopen - domain Ġsetting Ġ( i . e ., Ġwhere Ġminimal Ġor Ġno Ġcontextual Ġknowledge Ġis Ġexplicitly Ġprovided ), Ġrequires Ġbringing Ġto Ġbear Ġconsiderable Ġamount Ġof Ġbackground Ġknowledge Ġand Ġreasoning Ġabilities . ĠFor Ġexample , Ġknowing Ġthe Ġanswers Ġto Ġthe Ġtwo Ġquestions Ġin ĠFigure ĠFIG REF 1 Ġrequires Ġidentifying Ġa Ġspecific ĠIS A Ġrelation Ġ( i . e ., Ġthat Ġcooking Ġis Ġa Ġtype Ġof Ġlearned Ġbehavior ) Ġas Ġwell Ġas Ġrecalling Ġthe Ġdefinition Ġof Ġa Ġconcept Ġ( i . e ., Ġthat Ġglobal Ġwarming Ġis Ġdefined Ġas Ġa Ġworldwide Ġincrease Ġin Ġtemperature ). ĠIn Ġthe Ġmultiple - choice Ġsetting , Ġwhich Ġis Ġthe Ġvariety Ġof Ġquestion - ans w ering Ġ( Q A ) Ġthat Ġwe Ġfocus Ġon Ġin Ġthis Ġpaper , Ġthere Ġis Ġalso Ġpragmatic Ġreasoning Ġinvolved Ġin Ġselecting Ġoptimal Ġanswer Ġchoices Ġ( e . g ., Ġwhile Ġgreenhouse Ġeffect Ġmight Ġin Ġsome Ġother Ġcontext Ġbe Ġa Ġreasonable Ġanswer Ġto Ġthe Ġsecond Ġquestion Ġin ĠFigure ĠFIG REF 1 , Ġglobal Ġwarming Ġis Ġa Ġpreferable Ġcandidate ). </s> Recent Ġsuccesses Ġin ĠQ A , Ġdriven Ġlargely Ġby Ġthe Ġcreation Ġof Ġnew Ġresources ĠB IB REF 2 , ĠB IB REF 3 , ĠB IB REF 4 , ĠB IB REF 5 Ġand Ġadvances Ġin Ġmodel Ġpre - training ĠB IB REF 6 , ĠB IB REF 7 , Ġraise Ġa Ġnatural Ġquestion : Ġdo Ġstate - of - the - art Ġmultiple - choice ĠQ A Ġ( MC Q A ) Ġmodels Ġthat Ġexcel Ġat Ġstandard Ġtasks Ġreally Ġhave Ġbasic Ġknowledge Ġand Ġreasoning Ġskills ? </s> Most Ġexisting ĠMC Q A Ġdatasets Ġare Ġconstructed Ġthrough Ġeither Ġexpensive Ġcrowd - s ourcing ĠB IB REF 8 Ġor Ġhand Ġengineering Ġeffort , Ġin Ġthe Ġformer Ġcase Ġmaking Ġit Ġpossible Ġto Ġcollect Ġlarge Ġamounts Ġof Ġdata Ġat Ġthe Ġcost Ġof Ġlosing Ġsystematic Ġcontrol Ġover Ġthe Ġsemantics Ġof Ġthe Ġtarget Ġquestions . ĠHence , Ġdoing Ġa Ġcontrolled Ġexperiment Ġto Ġanswer Ġsuch Ġa Ġquestion Ġfor ĠQ A Ġis Ġdifficult Ġgiven Ġa Ġlack Ġof Ġtargeted Ġchallenge Ġdatasets . </s> Having Ġdefinitive Ġempirical Ġevidence Ġof Ġmodel Ġcompetence Ġon Ġany Ġgiven Ġphenomenon Ġrequires Ġconstructing Ġa Ġwide Ġrange Ġof Ġsystematic Ġtests . ĠFor Ġexample , Ġin Ġmeasuring Ġcompetence Ġof Ġdefinitions , Ġnot Ġonly Ġdo Ġwe Ġwant Ġto Ġsee Ġthat Ġthe Ġmodel Ġcan Ġhandle Ġindividual Ġquestions Ġsuch Ġas ĠFigure ĠFIG REF 1 . 1 Ġinside Ġof Ġbenchmark Ġtasks , Ġbut Ġthat Ġit Ġcan Ġanswer Ġa Ġwider Ġrange Ġof Ġquestions Ġthat Ġexhaust ively Ġcover Ġa Ġbroad Ġset Ġof Ġconcepts Ġand Ġquestion Ġpert urb ations Ġ( i . e ., Ġsystematic Ġadjustments Ġto Ġhow Ġthe Ġquestions Ġare Ġconstructed ). ĠThe Ġsame Ġapplies Ġto ĠIS A Ġreasoning ; Ġnot Ġonly Ġis Ġit Ġimportant Ġto Ġrecognize Ġin Ġthe Ġquestion Ġin ĠFigure ĠFIG REF 1 . 1 Ġthat Ġcooking Ġis Ġa Ġlearned Ġbehavior , Ġbut Ġalso Ġthat Ġcooking Ġis Ġa Ġgeneral Ġtype Ġof Ġbehavior Ġor , Ġthrough Ġa Ġfew Ġmore Ġinf erential Ġsteps , Ġa Ġtype Ġof Ġhuman Ġactivity . </s> In Ġthis Ġpaper , Ġwe Ġlook Ġat Ġsystematically Ġconstructing Ġsuch Ġtests Ġby Ġexploiting Ġthe Ġvast Ġamounts Ġof Ġstructured Ġinformation Ġcontained Ġin Ġvarious Ġtypes Ġof Ġexpert Ġknowledge Ġsuch Ġas Ġknowledge Ġgraphs Ġand Ġlex ical Ġtax onom ies . ĠOur Ġgeneral Ġmethodology Ġworks Ġas Ġillustrated Ġin ĠFigure ĠFIG REF 1 : Ġgiven Ġany ĠMC Q A Ġmodel Ġtrained Ġon Ġa Ġset Ġof Ġbenchmark Ġtasks , Ġwe Ġsystematically Ġgenerate Ġa Ġset Ġof Ġsynthetic Ġdataset Ġprobes Ġ( i . e ., ĠMC Q A Ġrender ings Ġof Ġthe Ġtarget Ġinformation ) Ġfrom Ġinformation Ġin Ġexpert Ġknowledge Ġsources . ĠWe Ġthen Ġuse Ġthese Ġprobes Ġto Ġask Ġtwo Ġempirical Ġquestions : Ġ1 ) Ġhow Ġwell Ġdo Ġmodels Ġtrained Ġon Ġbenchmark Ġtasks Ġperform Ġon Ġthese Ġprobing Ġtasks Ġand ; Ġ2 ) Ġcan Ġsuch Ġmodels Ġbe Ġre - trained Ġto Ġmaster Ġnew Ġchallenges Ġwith Ġminimal Ġperformance Ġloss Ġon Ġtheir Ġoriginal Ġtasks ? </s> While Ġour Ġmethodology Ġis Ġam enable Ġto Ġany Ġknowledge Ġsource Ġand Ġset Ġof Ġmodels / bench mark Ġtasks , Ġwe Ġfocus Ġon Ġprobing Ġstate - of - the - art Ġtransformer Ġmodels ĠB IB REF 7 , ĠB IB REF 9 Ġin Ġthe Ġdomain Ġof Ġscience ĠMC Q A . ĠFor Ġsources Ġof Ġexpert Ġknowledge , Ġwe Ġuse ĠWord Net , Ġa Ġcomprehensive Ġlex ical Ġont ology , Ġand Ġother Ġpublicly Ġavailable Ġdictionary Ġresources . ĠWe Ġdevise Ġprobes Ġthat Ġmeasure Ġmodel Ġcompetence Ġin Ġdefinition Ġand Ġtax onomic Ġknowledge Ġin Ġdifferent Ġsettings Ġ( including Ġhyper ny my , Ġhyp ony my , Ġand Ġsyn ony my Ġdetection , Ġand Ġword Ġsense Ġdis amb ig uation ). ĠThis Ġchoice Ġis Ġmotivated Ġby Ġfact Ġthat Ġthe Ġscience Ġdomain Ġis Ġconsidered Ġparticularly Ġchallenging Ġfor ĠQ A ĠB IB REF 10 , ĠB IB REF 11 , ĠB IB REF 12 , Ġand Ġexisting Ġscience Ġbenchmarks Ġare Ġknown Ġto Ġinvolve Ġwidespread Ġuse Ġof Ġsuch Ġknowledge Ġ( see ĠB IB REF 1 , ĠB IB REF 13 Ġfor Ġanalysis ), Ġwhich Ġis Ġalso Ġarguably Ġfundamental Ġto Ġmore Ġcomplex Ġforms Ġof Ġreasoning . </s> We Ġshow Ġthat Ġaccurately Ġprobing ĠQ A Ġmodels Ġvia Ġsynthetic Ġdatasets Ġis Ġnot Ġstraightforward , Ġas Ġunexpected Ġartifacts Ġcan Ġeasily Ġarise Ġin Ġsuch Ġdata . ĠThis Ġmotiv ates Ġour Ġcarefully Ġconstructed Ġbas elines Ġand Ġclose Ġdata Ġinspection Ġto Ġensure Ġprobe Ġquality . </s> Our Ġresults Ġconfirm Ġthat Ġtransformer - based ĠQ A Ġmodels Ġhave Ġa Ġremarkable Ġability Ġto Ġrecognize Ġcertain Ġtypes Ġof Ġknowledge Ġcaptured Ġin Ġour Ġprobes âĢĶ even Ġwithout Ġadditional Ġfine - tun ing . ĠSuch Ġmodels Ġcan Ġeven Ġoutper form Ġstrong Ġtask - specific Ġmodels Ġtrained Ġdirectly Ġon Ġour Ġprobing Ġtasks Ġ( e . g ., Ġon Ġdefinitions , Ġour Ġbest Ġmodel Ġachieves Ġ77 % Ġtest Ġaccuracy Ġwithout Ġspecialized Ġtraining , Ġas Ġopposed Ġto Ġ51 % Ġfor Ġa Ġtask - specific ĠL ST M - based Ġmodel ). ĠWe Ġalso Ġshow Ġthat Ġthe Ġsame Ġmodels Ġcan Ġbe Ġeffectively Ġre - fine - tun ed Ġon Ġsmall Ġsamples Ġ( even Ġ100 Ġexamples ) Ġof Ġprobe Ġdata , Ġand Ġthat Ġhigh Ġperformance Ġon Ġthe Ġprobes Ġtends Ġto Ġcorrelate Ġwith Ġa Ġsmaller Ġdrop Ġin Ġthe Ġmodel 's Ġperformance Ġon Ġthe Ġoriginal ĠQ A Ġtask . </s> Our Ġcomprehensive Ġassessment Ġreveals Ġseveral Ġinteresting Ġnuances Ġto Ġthe Ġoverall Ġpositive Ġtrend . ĠFor Ġexample , Ġthe Ġperformance Ġof Ġeven Ġthe Ġbest ĠQ A Ġmodels Ġde grades Ġsubstantially Ġon Ġour Ġhyp onym Ġprobes Ġ( by Ġ8 - 15 %) Ġwhen Ġgoing Ġfrom Ġ1 - hop Ġlinks Ġto Ġ2 - hops . ĠFurther , Ġthe Ġaccuracy Ġof Ġeven Ġour Ġbest Ġmodels Ġon Ġthe ĠWord Net Q A Ġprobe Ġdrops Ġby Ġ14 - 44 % Ġunder Ġour Ġcluster - based Ġanalysis , Ġwhich Ġassess es Ġwhether Ġa Ġmodel Ġknows Ġseveral Ġfacts Ġabout Ġeach Ġindividual Ġconcept , Ġrather Ġthan Ġjust Ġbeing Ġgood Ġat Ġanswering Ġisolated Ġquestions . ĠState - of - the - art ĠQ A Ġmodels Ġthus Ġhave Ġmuch Ġroom Ġto Ġimprove Ġeven Ġin Ġsome Ġfundamental Ġbuilding Ġblocks , Ġnamely Ġdefinitions Ġand Ġtax onomic Ġhierarch ies , Ġof Ġmore Ġcomplex Ġforms Ġof Ġreasoning . </s> Related ĠWork </s> We Ġfollow Ġrecent Ġwork Ġon Ġconstructing Ġchallenge Ġdatasets Ġfor Ġprobing Ġneural Ġmodels , Ġwhich Ġhas Ġprimarily Ġfocused Ġon Ġthe Ġtask Ġof Ġnatural Ġlanguage Ġinference Ġ( N LI ) ĠB IB REF 14 , ĠB IB REF 15 , ĠB IB REF 16 , ĠB IB REF 17 , ĠB IB REF 18 . ĠMost Ġof Ġthis Ġwork Ġlooks Ġat Ġconstructing Ġdata Ġthrough Ġadvers arial Ġgeneration Ġmethods , Ġwhich Ġhave Ġalso Ġbeen Ġfound Ġuseful Ġfor Ġcreating Ġstronger Ġmodels ĠB IB REF 19 . ĠThere Ġhas Ġalso Ġbeen Ġwork Ġon Ġusing Ġsynthetic Ġdata Ġof Ġthe Ġtype Ġwe Ġconsider Ġin Ġthis Ġpaper ĠB IB REF 20 , ĠB IB REF 21 , ĠB IB REF 22 . ĠWe Ġclosely Ġfollow Ġthe Ġmethodology Ġof ĠB IB REF 22 , Ġwho Ġuse Ġhand - con structed Ġlinguistic Ġfragments Ġto Ġprobe ĠNL I Ġmodels Ġand Ġstudy Ġmodel Ġre - training Ġusing Ġa Ġvariant Ġof Ġthe Ġinoc ulation Ġby Ġfine - tun ing Ġstrategy Ġof ĠB IB REF 23 . ĠIn Ġcontrast , Ġwe Ġfocus Ġon Ġprobing Ġopen - domain ĠMC Q A Ġmodels Ġ( see ĠB IB REF 24 Ġfor Ġa Ġrelated Ġstudy Ġin Ġthe Ġreading Ġcomprehension Ġsetting ) Ġas Ġwell Ġas Ġconstructing Ġdata Ġfrom Ġmuch Ġlarger Ġsources Ġof Ġstructured Ġknowledge . </s> Our Ġmain Ġstudy Ġfocuses Ġon Ġprobing Ġthe ĠB ERT Ġmodel Ġand Ġfine - tun ing Ġapproach Ġof ĠB IB REF 7 , Ġand Ġother Ġvariants Ġthereof , Ġwhich Ġare Ġall Ġbased Ġon Ġthe Ġtransformer Ġarchitecture Ġof ĠB IB REF 25 . ĠRelated Ġto Ġour Ġefforts , Ġthere Ġhave Ġbeen Ġrecent Ġstudies Ġinto Ġthe Ġtypes Ġof Ġrelational Ġknowledge Ġcontained Ġin Ġlarge - scale Ġknowledge Ġmodels ĠB IB REF 26 , ĠB IB REF 27 , ĠB IB REF 28 , Ġwhich , Ġsimilar Ġto Ġour Ġwork , Ġprobe Ġmodels Ġusing Ġstructured Ġknowledge Ġsources . ĠThis Ġprior Ġwork , Ġhowever , Ġprimarily Ġfocuses Ġon Ġun ear thing Ġthe Ġknowledge Ġcontained Ġin Ġthe Ġunderlying Ġlanguage Ġmodels Ġas Ġis Ġwithout Ġfurther Ġtraining , Ġusing Ġsimple Ġ( single Ġtoken ) Ġclo ze - style Ġprobing Ġtasks Ġand Ġtemplates Ġ( similar Ġto Ġwhat Ġwe Ġpropose Ġin ĠSection ĠSEC REF 3 ). ĠIn Ġcontrast , Ġwe Ġfocus Ġon Ġunderstanding Ġthe Ġknowledge Ġcontained Ġin Ġlanguage Ġmodels Ġafter Ġthey Ġhave Ġbeen Ġtrained Ġfor Ġa ĠQ A Ġend - task Ġusing Ġbenchmark Ġdatasets Ġin Ġwhich Ġsuch Ġknowledge Ġis Ġexpected Ġto Ġbe Ġwidespread . ĠFurther , Ġour Ġevaluation Ġis Ġdone Ġbefore Ġand Ġafter Ġthese Ġmodels Ġare Ġfine - tun ed Ġon Ġour Ġprobe ĠQ A Ġtasks , Ġusing Ġa Ġmore Ġcomplex Ġset Ġof ĠQ A Ġtemplates Ġand Ġtarget Ġinf erences . </s> The Ġuse Ġof Ġlex ical Ġresources Ġand Ġknowledge Ġgraphs Ġsuch Ġas ĠWord Net Ġto Ġconstruct Ġdatasets Ġhas Ġa Ġlong Ġhistory , Ġand Ġhas Ġrecently Ġappeared Ġin Ġwork Ġon Ġadvers arial Ġattacks ĠB IB REF 14 , ĠB IB REF 29 Ġand Ġgeneral Ġtask Ġconstruction ĠB IB REF 30 , ĠB IB REF 31 . ĠIn Ġthe Ġarea Ġof ĠMC Q A , Ġthere Ġis Ġrelated Ġwork Ġon Ġconstructing Ġquestions Ġfrom Ġtu ples ĠB IB REF 32 , ĠB IB REF 3 , Ġboth Ġof Ġwhich Ġinvolve Ġstandard Ġcrowd Ġannotation Ġto Ġelicit Ġquestion - answer Ġpairs Ġ( see Ġalso ĠB IB REF 33 , ĠB IB REF 34 ). ĠIn Ġcontrast Ġto Ġthis Ġwork , Ġwe Ġfocus Ġon Ġgenerating Ġdata Ġin Ġan Ġentirely Ġautomatic Ġfashion , Ġwhich Ġob vi ates Ġthe Ġneed Ġfor Ġexpensive Ġannotation Ġand Ġgives Ġus Ġthe Ġflexibility Ġto Ġconstruct Ġmuch Ġlarger Ġdatasets Ġthat Ġcontrol Ġa Ġrich Ġset Ġof Ġsemantic Ġaspects Ġof Ġthe Ġtarget Ġquestions . </s> Dat as et ĠPro bes Ġand ĠConstruction </s> Our Ġprobing Ġmethodology Ġstarts Ġby Ġconstructing Ġchallenge Ġdatasets Ġ( Figure ĠFIG REF 1 , Ġyellow Ġbox ) Ġfrom Ġa Ġtarget Ġset Ġof Ġknowledge Ġresources . ĠEach Ġof Ġour Ġprobing Ġdatasets Ġconsists Ġof Ġmultiple - choice Ġquestions Ġthat Ġinclude Ġa Ġquestion Ġ$\ text bf Ġ{ q } $ Ġand Ġa Ġset Ġof Ġanswer Ġchoices Ġor Ġcandidates Ġ$\ l brace Ġa _{ 1 }, ... a _{ N }\ r brace Ġ$ . ĠThis Ġsection Ġdescribes Ġin Ġdetail Ġthe Ġ5 Ġdifferent Ġdatasets Ġwe Ġbuild , Ġwhich Ġare Ġdrawn Ġfrom Ġtwo Ġsources Ġof Ġexpert Ġknowledge , Ġnamely ĠWord Net ĠB IB REF 35 Ġand Ġthe ĠGNU ĠCollabor ative ĠInternational ĠDictionary Ġof ĠEnglish Ġ( GC IDE ). ĠWe Ġdescribe Ġeach Ġresource Ġin Ġturn , Ġand Ġexplain Ġhow Ġthe Ġresulting Ġdataset Ġprobes , Ġwhich Ġwe Ġcall ĠWord Net Q A Ġand ĠDictionary Q A , Ġare Ġconstructed . </s> For Ġconvenience , Ġwe Ġwill Ġdescribe Ġeach Ġsource Ġof Ġexpert Ġknowledge Ġas Ġa Ġdirected , Ġedge - label ed Ġgraph Ġ$ G $. ĠThe Ġnodes Ġof Ġthis Ġgraph Ġare Ġ$\ math cal Ġ{ V } Ġ= Ġ\ math cal Ġ{ C } Ġ\ cup Ġ\ math cal Ġ{ W } Ġ\ cup Ġ\ math cal Ġ{ S } Ġ\ cup Ġ\ math cal Ġ{ D } $, Ġwhere Ġ$\ math cal Ġ{ C } $ Ġis Ġa Ġset Ġof Ġatomic Ġconcepts , Ġ$\ math cal Ġ{ W } $ Ġa Ġset Ġof Ġwords , Ġ$\ math cal Ġ{ S } $ Ġa Ġset Ġof Ġsentences , Ġand Ġ$\ math cal Ġ{ D } $ Ġa Ġset Ġof Ġdefinitions Ġ( see ĠTable ĠT AB REF 4 Ġfor Ġdetails Ġfor ĠWord Net Ġand ĠGC IDE ). ĠEach Ġedge Ġof Ġ$ G $ Ġis Ġdirected Ġfrom Ġan Ġatomic Ġconcept Ġin Ġ$\ math cal Ġ{ C } $ Ġto Ġanother Ġnode Ġin Ġ$ V $, Ġand Ġis Ġlabeled Ġwith Ġa Ġrelation , Ġsuch Ġas Ġhyper ny m Ġor Ġis a $ ^ \ up arrow Ġ$ , Ġfrom Ġa Ġset Ġof Ġrelations Ġ$\ math cal Ġ{ R } $ Ġ( see ĠTable ĠT AB REF 4 ). </s> When Ġdefining Ġour Ġprobe Ġquestion Ġtemplates , Ġit Ġwill Ġbe Ġuseful Ġto Ġview Ġ$ G $ Ġas Ġa Ġset Ġof Ġ( relation , Ġsource , Ġtarget ) Ġtri ples Ġ$\ math cal Ġ{ T } Ġ\ sub set eq Ġ\ math cal Ġ{ R } Ġ\ times Ġ\ math cal Ġ{ C } Ġ\ times Ġ\ math cal Ġ{ V } $. ĠDue Ġto Ġtheir Ġorigin Ġin Ġan Ġexpert Ġknowledge Ġsource , Ġsuch Ġtri ples Ġpreserve Ġsemantic Ġconsistency . ĠFor Ġinstance , Ġwhen Ġthe Ġrelation Ġin Ġa Ġtriple Ġis Ġdef , Ġthe Ġcorresponding Ġedge Ġmaps Ġa Ġconcept Ġin Ġ$\ math cal Ġ{ C } $ Ġto Ġa Ġdefinition Ġin Ġ$\ math cal Ġ{ D } $. </s> To Ġconstruct Ġprobe Ġdatasets , Ġwe Ġrely Ġon Ġtwo Ġhe uristic Ġfunctions , Ġdefined Ġbelow Ġfor Ġeach Ġindividual Ġprobe : Ġ$\ text sc Ġ{ gen } _{ \ math cal Ġ{ Q }} (\ t au Ġ) $, Ġwhich Ġgenerates Ġgold Ġquestion - answer Ġpairs Ġ$( \ text bf Ġ{ q }, \ text bf Ġ{ a }) $ Ġfrom Ġa Ġset Ġof Ġtri ples Ġ$\ t au Ġ\ sub set eq Ġ\ math cal Ġ{ T } $ Ġand Ġquestion Ġtemplates Ġ$\ math cal Ġ{ Q } $, Ġand Ġ$\ text sc Ġ{ dist r } (\ t au Ġ^ {\ prime Ġ}) $, Ġwhich Ġgenerates Ġdistract or Ġanswers Ġchoices Ġ$\ l brace Ġa ^ {\ prime Ġ} _{ 1 }, ... a ^ {\ prime Ġ} _{ N - 1 } Ġ\ r brace Ġ$ Ġbased Ġon Ġanother Ġset Ġof Ġtri ples Ġ$\ t au Ġ^ {\ prime Ġ} $ Ġ( where Ġusually Ġ$\ t au Ġ\ sub set Ġ\ t au Ġ^ {\ prime Ġ} $ ). ĠFor Ġbre vity , Ġwe Ġwill Ġuse Ġ$\ text sc Ġ{ gen } (\ t au Ġ) $ Ġto Ġdenote Ġ$\ text sc Ġ{ gen } _{ \ math cal Ġ{ Q }} (\ t au Ġ) $, Ġleaving Ġquestion Ġtemplates Ġ$\ math cal Ġ{ Q } $ Ġimplicit . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠWord Net Q A </s> Word Net Ġis Ġan ĠEnglish Ġlex ical Ġdatabase Ġconsisting Ġof Ġaround Ġ117 k Ġconcepts , Ġwhich Ġare Ġorganized Ġinto Ġgroups Ġof Ġsy ns ets Ġthat Ġeach Ġcontain Ġa Ġgloss Ġ( i . e ., Ġa Ġdefinition Ġof Ġthe Ġtarget Ġconcept ), Ġa Ġset Ġof Ġrepresentative ĠEnglish Ġwords Ġ( called Ġle mm as ), Ġand , Ġin Ġaround Ġ33 k Ġsy ns ets , Ġexample Ġsentences . ĠIn Ġaddition , Ġmany Ġsy ns ets Ġhave ĠIS A Ġlinks Ġto Ġother Ġsy ns ets Ġthat Ġexpress Ġcomplex Ġtax onomic Ġrelations . ĠFigure ĠFIG REF 6 Ġshows Ġan Ġexample Ġand ĠTable ĠT AB REF 4 Ġsummarizes Ġhow Ġwe Ġformulate ĠWord Net Ġas Ġa Ġset Ġof Ġtri ples Ġ$\ math cal Ġ{ T } $ Ġof Ġvarious Ġtypes . ĠThese Ġtri ples Ġtogether Ġrepresent Ġa Ġdirected , Ġedge - label ed Ġgraph Ġ$ G $. ĠOur Ġmain Ġmotivation Ġfor Ġusing ĠWord Net , Ġas Ġopposed Ġto Ġa Ġresource Ġsuch Ġas ĠConcept Net ĠB IB REF 36 , Ġis Ġthe Ġavailability Ġof Ġgloss es Ġ($ \ math cal Ġ{ D } $ ) Ġand Ġexample Ġsentences Ġ($ \ math cal Ġ{ S } $ ), Ġwhich Ġallows Ġus Ġto Ġconstruct Ġnatural Ġlanguage Ġquestions Ġthat Ġcontextual ize Ġthe Ġtypes Ġof Ġconcepts Ġwe Ġwant Ġto Ġprobe . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠWord Net Q A Ġ: :: ĠExample ĠGeneration Ġ@ ! ST ART @ $ \ text sc Ġ{ gen } (\ t au Ġ) $ @ ! END @ . </s> We Ġbuild Ġ4 Ġindividual Ġdatasets Ġbased Ġon Ġsemantic Ġrelations Ġnative Ġto ĠWord Net Ġ( see ĠB IB REF 37 ): Ġhyper ny my Ġ( i . e ., Ġgeneral ization Ġor ĠIS A Ġreasoning Ġup Ġa Ġtax onomy , ĠIS A $ ^ \ up arrow Ġ$ ), Ġhyp ony my Ġ( ISA $ ^ {\ down arrow Ġ} $ ), Ġsyn ony my , Ġand Ġdefinitions . ĠTo Ġgenerate Ġa Ġset Ġof Ġquestions Ġin Ġeach Ġcase , Ġwe Ġemploy Ġa Ġnumber Ġof Ġrule Ġtemplates Ġ$\ math cal Ġ{ Q } $ Ġthat Ġoperate Ġover Ġtu ples . ĠA Ġsubset Ġof Ġsuch Ġtemplates Ġis Ġshown Ġin ĠTable ĠT AB REF 8 . ĠThe Ġtemplates Ġwere Ġdesigned Ġto Ġmimic Ġnatural istic Ġquestions Ġwe Ġobserved Ġin Ġour Ġscience Ġbenchmarks . </s> For Ġexample , Ġsuppose Ġwe Ġwish Ġto Ġcreate Ġa Ġquestion Ġ$\ text bf Ġ{ q } $ Ġabout Ġthe Ġdefinition Ġof Ġa Ġtarget Ġconcept Ġ$ c Ġ\ in Ġ\ math cal Ġ{ C } $. ĠWe Ġfirst Ġselect Ġa Ġquestion Ġtemplate Ġfrom Ġ$\ math cal Ġ{ Q } $ Ġthat Ġfirst Ġintroduces Ġthe Ġconcept Ġ$ c $ Ġand Ġits Ġle mma Ġ$ l Ġ\ in Ġ\ math cal Ġ{ W } $ Ġin Ġcontext Ġusing Ġthe Ġexample Ġsentence Ġ$ s Ġ\ in Ġ\ math cal Ġ{ S } $, Ġand Ġthen Ġasks Ġto Ġidentify Ġthe Ġcorresponding ĠWord Net Ġgloss Ġ$ d Ġ\ in Ġ\ math cal Ġ{ D } $, Ġwhich Ġserves Ġas Ġthe Ġgold Ġanswer Ġ$\ text bf Ġ{ a } $. ĠThe Ġsame Ġis Ġdone Ġfor ĠIS A Ġreasoning ; Ġeach Ġquestion Ġabout Ġa Ġhyper ny m / hyp onym Ġrelation Ġbetween Ġtwo Ġconcepts Ġ$ c Ġ\ right arrow Ġ^ {\ up arrow Ġ/ \ down arrow Ġ} Ġc ^ {\ prime Ġ} Ġ\ in Ġ\ math cal Ġ{ T } _{ i } $ Ġ( e . g ., Ġ$\ text tt Ġ{ dog } Ġ\ right arrow Ġ^ {\ up arrow Ġ/ \ down arrow Ġ} Ġ\ text tt Ġ{ animal / ter rier } $ ) Ġfirst Ġintroduces Ġa Ġcontext Ġfor Ġ$ c $ Ġand Ġthen Ġasks Ġfor Ġan Ġanswer Ġthat Ġidentifies Ġ$ c ^ {\ prime Ġ} $ Ġ( which Ġis Ġalso Ġprovided Ġwith Ġa Ġgloss Ġso Ġas Ġto Ġcontain Ġall Ġavailable Ġcontext ). </s> In Ġthe Ġlatter Ġcase , Ġthe Ġrules Ġ$( \ text tt Ġ{ isa } ^{ r }, c , c ^ {\ prime Ġ}) Ġ\ in Ġ\ math cal Ġ{ T } _ i $ Ġin ĠTable ĠT AB REF 8 Ġcover Ġonly Ġdirect ĠIS A Ġlinks Ġfrom Ġ$ c $ Ġin Ġdirection Ġ$ r Ġ\ in Ġ\ l brace Ġ\ up arrow Ġ, \ down arrow Ġ\ r brace Ġ$ . ĠIn Ġpractice , Ġfor Ġeach Ġ$ c $ Ġand Ġdirection Ġ$ r $, Ġwe Ġconstruct Ġtests Ġthat Ġcover Ġthe Ġset ĠH OPS $ ( c , r ) $ Ġof Ġall Ġdirect Ġas Ġwell Ġas Ġderived ĠIS A Ġrelations Ġof Ġ$ c $ : </s> This Ġallows Ġus Ġto Ġevaluate Ġthe Ġextent Ġto Ġwhich Ġmodels Ġare Ġable Ġto Ġhandle Ġcomplex Ġforms Ġof Ġreasoning Ġthat Ġrequire Ġseveral Ġinf erential Ġsteps Ġor Ġhops . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠWord Net Q A Ġ: :: ĠDist ractor ĠGeneration : Ġ@ ! ST ART @ $ \ text sc Ġ{ dist r } (\ t au Ġ^ {\ prime Ġ}) $ @ ! END @ . </s> An Ġexample Ġof Ġhow Ġdistract ors Ġare Ġgenerated Ġis Ġshown Ġin ĠFigure ĠFIG REF 6 , Ġwhich Ġrelies Ġon Ġsimilar Ġprinciples Ġas Ġabove . ĠFor Ġeach Ġconcept Ġ$ c $, Ġwe Ġchoose Ġ4 Ġdistract or Ġanswers Ġthat Ġare Ġclose Ġin Ġthe ĠWord Net Ġsemantic Ġspace . ĠFor Ġexample , Ġwhen Ġconstructing Ġhyper ny my Ġtests Ġfor Ġ$ c $ Ġfrom Ġthe Ġset Ġhops $ ( c , \ up arrow Ġ) $, Ġwe Ġbuild Ġdistract ors Ġby Ġdrawing Ġfrom Ġ$\ text sc Ġ{ hops } ( c , \ down arrow Ġ) $ Ġ( and Ġvice Ġversa ), Ġas Ġwell Ġas Ġfrom Ġthe Ġ$\ ell Ġ$ - deep Ġsister Ġfamily Ġof Ġ$ c $, Ġdefined Ġas Ġfollows . ĠThe Ġ1 - deep Ġsister Ġfamily Ġis Ġsimply Ġ$ c $ ' s Ġsiblings Ġor Ġsisters , Ġi . e ., Ġthe Ġother Ġchildren Ġ$\ t ilde { c } Ġ\ ne Ġc $ Ġof Ġthe Ġparent Ġnode Ġ$ c ^ {\ prime Ġ} $ Ġof Ġ$ c $. ĠFor Ġ$\ ell Ġ> Ġ1 $, Ġthe Ġ$\ ell Ġ$ - deep Ġsister Ġfamily Ġalso Ġincludes Ġall Ġdescendants Ġof Ġeach Ġ$\ t ilde { c } $ Ġup Ġto Ġ$\ ell Ġ- 1 $ Ġlevels Ġdeep , Ġden oted Ġ$\ text sc Ġ{ hops } _{ \ ell Ġ- 1 } (\ t ilde { c }, \ down arrow Ġ) $. ĠForm ally : </s> For Ġdefinitions Ġand Ġsyn onyms Ġwe Ġbuild Ġdistract ors Ġfrom Ġall Ġof Ġthese Ġsets Ġ( with Ġa Ġsimilar Ġrestriction Ġon Ġthe Ġdepth Ġof Ġsister Ġdistract ors Ġas Ġnoted Ġabove ). ĠIn Ġdoing Ġthis , Ġwe Ġcan Ġsystematically Ġinvestigate Ġmodel Ġperformance Ġon Ġa Ġwide Ġrange Ġof Ġdistract or Ġsets . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠWord Net Q A Ġ: :: ĠP ert urb ations Ġand ĠSem antic ĠCl usters </s> Based Ġon Ġhow Ġwe Ġgenerate Ġdata , Ġfor Ġeach Ġconcept Ġ$ c $ Ġ( i . e ., Ġatomic ĠWord Net Ġsyn set ) Ġand Ġprobe Ġtype Ġ( i . e ., Ġdefinitions , Ġhyper ny my , Ġetc .), Ġwe Ġhave Ġa Ġwide Ġvariety Ġof Ġquestions Ġrelated Ġto Ġ$ c $ Ġthat Ġmanipulate Ġ1 ) Ġthe Ġcomplexity Ġof Ġreasoning Ġthat Ġis Ġinvolved Ġ( e . g ., Ġthe Ġnumber Ġof Ġinf erential Ġhops ) Ġand ; Ġ2 ) Ġthe Ġtypes Ġof Ġdistract ors Ġ( or Ġdistract or Ġpert urb ations ) Ġthat Ġare Ġemployed . ĠWe Ġcall Ġsuch Ġsets Ġsemantic Ġclusters . ĠAs Ġwe Ġdescribe Ġin Ġthe Ġnext Ġsection , Ġsemantic Ġclusters Ġallow Ġus Ġto Ġdevise Ġnew Ġtypes Ġof Ġevaluation Ġthat Ġreveal Ġwhether Ġmodels Ġhave Ġcomprehensive Ġand Ġconsistent Ġknowledge Ġof Ġtarget Ġconcepts Ġ( e . g ., Ġevaluating Ġwhether Ġa Ġmodel Ġcan Ġcorrectly Ġanswer Ġseveral Ġquestions Ġassociated Ġwith Ġa Ġconcept , Ġas Ġopposed Ġto Ġa Ġfew Ġdis j oint Ġinstances ). </s> Details Ġof Ġthe Ġindividual Ġdatasets Ġare Ġshown Ġin ĠTable ĠT AB REF 12 . ĠFrom Ġthese Ġsets , Ġwe Ġfollow ĠB IB REF 22 Ġin Ġall ocating Ġa Ġmaximum Ġof Ġ3 k Ġexamples Ġfor Ġtraining Ġand Ġreserve Ġthe Ġrest Ġfor Ġdevelopment Ġand Ġtesting . ĠSince Ġwe Ġare Ġinterested Ġin Ġprobing , Ġhaving Ġlarge Ġheld - out Ġsets Ġallows Ġus Ġto Ġdo Ġdetailed Ġanalysis Ġand Ġcluster - based Ġevaluation . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠDictionary Q A </s> The ĠDictionary Q A Ġdataset Ġis Ġcreated Ġfrom Ġthe ĠGC IDE Ġdictionary , Ġwhich Ġis Ġa Ġcomprehensive Ġopen - source ĠEnglish Ġdictionary Ġbuilt Ġlargely Ġfrom Ġthe ĠWebster 's ĠRevised ĠUn ab rid ged ĠDictionary ĠB IB REF 38 . ĠEach Ġentry Ġconsists Ġof Ġa Ġword , Ġits Ġpart - of - speech , Ġits Ġdefinition , Ġand Ġan Ġoptional Ġexample Ġsentence Ġ( see ĠTable ĠT AB REF 14 ). ĠOverall , Ġ33 k Ġentries Ġ( out Ġof Ġa Ġtotal Ġof Ġ155 k ) Ġcontain Ġexample Ġsentences / us ages . ĠAs Ġwith Ġthe ĠWord Net Ġprobes , Ġwe Ġfocus Ġon Ġthis Ġsubset Ġso Ġas Ġto Ġcontextual ize Ġeach Ġword Ġbeing Ġprob ed . ĠIn Ġcontrast Ġto ĠWord Net , ĠGC IDE Ġdoes Ġnot Ġhave ĠIS A Ġrelations Ġor Ġexplicit Ġsy ns ets , Ġso Ġwe Ġtake Ġeach Ġunique Ġentry Ġto Ġbe Ġa Ġdistinct Ġsense . ĠWe Ġthen Ġuse Ġthe Ġdictionary Ġentries Ġto Ġcreate Ġa Ġprobe Ġthat Ġcenters Ġaround Ġword - sense Ġdis amb ig uation , Ġas Ġdescribed Ġbelow . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠDictionary Q A Ġ: :: ĠExample Ġand ĠDist ractor ĠGeneration . </s> To Ġgenerate Ġgold Ġquestions Ġand Ġanswers , Ġwe Ġuse Ġthe Ġsame Ġgeneration Ġtemplates Ġfor Ġdefinitions Ġexempl ified Ġin ĠFigure ĠT AB REF 8 Ġfor ĠWord Net Q A . ĠTo Ġgenerate Ġdistract ors , Ġwe Ġsimply Ġtake Ġalternative Ġdefinitions Ġfor Ġthe Ġtarget Ġwords Ġthat Ġrepresent Ġa Ġdifferent Ġword Ġsense Ġ( e . g ., Ġthe Ġalternative Ġdefinitions Ġof Ġgift Ġshown Ġin ĠTable ĠT AB REF 14 ), Ġas Ġwell Ġas Ġrandomly Ġchosen Ġdefinitions Ġif Ġneeded Ġto Ġcreate Ġa Ġ5 - way Ġmultiple Ġchoice Ġquestion . ĠAs Ġabove , Ġwe Ġreserve Ġa Ġmaximum Ġof Ġ3 k Ġexamples Ġfor Ġtraining . ĠSince Ġwe Ġhave Ġonly Ġ9 k Ġexamples Ġin Ġtotal Ġin Ġthis Ġdataset Ġ( see ĠWord Sense Ġin ĠTable ĠT AB REF 12 ), Ġwe Ġalso Ġreserve Ġ3 k Ġeach Ġfor Ġdevelopment Ġand Ġtesting . </s> We Ġnote Ġthat Ġinitial Ġattempts Ġto Ġbuild Ġthis Ġdataset Ġthrough Ġstandard Ġrandom Ġsplitting Ġgave Ġrise Ġto Ġcertain Ġsystematic Ġbiases Ġthat Ġwere Ġexploited Ġby Ġthe Ġchoice - only Ġbaseline Ġmodels Ġdescribed Ġin Ġthe Ġnext Ġsection , Ġand Ġhence Ġinflated Ġoverall Ġmodel Ġscores . ĠAfter Ġseveral Ġefforts Ġat Ġfiltering Ġwe Ġfound Ġthat , Ġamong Ġother Ġfactors , Ġusing Ġdefinitions Ġfrom Ġentries Ġwithout Ġexample Ġsentences Ġas Ġdistract ors Ġ( e . g ., Ġthe Ġfirst Ġtwo Ġentries Ġin ĠTable ĠT AB REF 14 ) Ġhad Ġa Ġsurprising Ġcorrelation Ġwith Ġsuch Ġbiases . ĠThis Ġsuggests Ġthat Ġpossible Ġbiases Ġinvolving Ġdifferences Ġbetween Ġdictionary Ġentries Ġwith Ġand Ġwithout Ġexamples Ġcan Ġt aint Ġthe Ġresulting Ġautomatically Ġgenerated ĠMC Q A Ġdataset Ġ( for Ġmore Ġdiscussion Ġon Ġthe Ġpitfalls Ġinvolved Ġwith Ġautomatic Ġdataset Ġconstruction , Ġsee ĠSection ĠSEC REF 5 ). </s> Pro bing ĠMethod ology Ġand ĠModel ing </s> Given Ġthe Ġprobes Ġabove , Ġwe Ġnow Ġcan Ġstart Ġto Ġanswer Ġthe Ġempirical Ġquestions Ġposed Ġat Ġthe Ġbeginning . ĠOur Ġmain Ġfocus Ġis Ġon Ġlooking Ġat Ġtransformer - based ĠMC Q A Ġmodels Ġtrained Ġin Ġthe Ġscience Ġdomain Ġ( using Ġthe Ġbenchmarks Ġshown Ġin ĠTable ĠT AB REF 21 ). ĠIn Ġthis Ġsection , Ġwe Ġprovide Ġdetails Ġof ĠMC Q A Ġand Ġthe Ġtarget Ġmodels , Ġas Ġwell Ġas Ġseveral Ġbas elines Ġthat Ġwe Ġuse Ġto Ġsanity Ġcheck Ġour Ġnew Ġdatasets . ĠTo Ġevaluate Ġmodel Ġcompetence , Ġwe Ġlook Ġat Ġa Ġcombination Ġof Ġmodel Ġperformance Ġafter Ġscience Ġpre - training Ġand Ġafter Ġadditional Ġmodel Ġfine - tun ing Ġusing Ġthe Ġloss less Ġinoc ulation Ġstrategy Ġof ĠB IB REF 22 Ġ( Section ĠSEC REF 22 ). ĠIn ĠSection ĠSEC REF 24 , Ġwe Ġalso Ġdiscuss Ġa Ġcluster - level Ġaccuracy Ġmetric Ġfor Ġmeasuring Ġperformance Ġover Ġsemantic Ġclusters . </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠTask ĠDefinition Ġand ĠModel ing </s> Given Ġa Ġdataset Ġ$ D Ġ= \ l brace Ġ( \ text bf Ġ{ q } ^{ ( d ) }, Ġ\ l brace Ġa _{ 1 } ^{ ( d ) }, ... , Ġa _{ N } ^{ ( d ) }\ r brace Ġ) Ġ\ r brace Ġ_ { d } ^ {\ mid ĠD Ġ\ mid Ġ} $ Ġconsisting Ġof Ġpairs Ġof Ġquestions Ġstems Ġ$\ text bf Ġ{ q } $ Ġand Ġanswer Ġchoices Ġ$ a _{ i } $, Ġthe Ġgoal Ġis Ġto Ġfind Ġthe Ġcorrect Ġanswer Ġ$ a _{ i ^{ * }} $ Ġthat Ġcorrectly Ġanswers Ġeach Ġ$\ text bf Ġ{ q } $. ĠThroughout Ġthis Ġpaper , Ġwe Ġlook Ġat Ġ5 - way Ġmultiple - choice Ġproblems Ġ( i . e ., Ġwhere Ġeach Ġ$ N = 5 $ ). </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠTask ĠDefinition Ġand ĠModel ing Ġ: :: ĠQuestion + Answer ĠEnc oder . </s> To Ġmodel Ġthis , Ġour Ġinvestigation Ġcenters Ġaround Ġthe Ġuse Ġof Ġthe Ġtransformer - based ĠB IB REF 25 ĠB ERT Ġenc oder Ġand Ġfine - tun ing Ġapproach Ġof ĠB IB REF 7 Ġ( see Ġalso ĠB IB REF 6 ). ĠFor Ġeach Ġquestion Ġand Ġindividual Ġanswer Ġpair Ġ$ q ^{ ( j )} _{ a _{ i }} $, Ġwe Ġassume Ġthe Ġfollowing Ġrendering Ġof Ġthis Ġinput : </s> which Ġis Ġrun Ġthrough Ġthe Ġpre - trained ĠB ERT Ġenc oder Ġto Ġgenerate Ġa Ġrepresentation Ġfor Ġ$ Ġq ^{ ( j )} _{ a _{ i }} $ Ġusing Ġthe Ġhidden Ġstate Ġrepresentation Ġfor ĠCLS Ġ( i . e ., Ġthe Ġclass ifier Ġtoken ) Ġ$\ text bf Ġ{ c } _{ i } $ : </s> The Ġprobability Ġof Ġa Ġgiven Ġanswer Ġ$ p ^{ ( j )} _{ i } $ Ġis Ġthen Ġcomputed Ġas Ġ$ p ^{ ( j )} _{ i } Ġ\ pro pt o Ġe ^ {\ text bf Ġ{ v }\ cd ot Ġ\ text bf Ġ{ c } ^{ ( j )} _{ i }} $, Ġwhich Ġuses Ġan Ġadditional Ġset Ġof Ġclassification Ġparameters Ġ$\ text bf Ġ{ v } Ġ\ in Ġ\ math bb Ġ{ R } ^{ H } $ Ġthat Ġare Ġoptimized Ġ( along Ġwith Ġthe Ġfull Ġtransformer Ġnetwork ) Ġby Ġtaking Ġthe Ġfinal Ġloss Ġof Ġthe Ġprobability Ġof Ġeach Ġcorrect Ġanswer Ġ$ p _{ i ^{ * }} $ Ġover Ġall Ġanswer Ġchoices : </s> We Ġspecifically Ġuse ĠB ERT - large Ġunc ased Ġwith Ġwhole - word Ġmask ing , Ġas Ġwell Ġas Ġthe ĠRo BER Ta - large Ġmodel Ġfrom ĠB IB REF 9 , Ġwhich Ġis Ġa Ġmore Ġrobust ly Ġtrained Ġversion Ġof Ġthe Ġoriginal ĠB ERT Ġmodel . ĠOur Ġsystem Ġuses Ġthe Ġimplementations Ġprovided Ġin ĠAllen N LP ĠB IB REF 39 Ġand ĠHug ging face ĠB IB REF 40 . </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠTask ĠDefinition Ġand ĠModel ing Ġ: :: ĠBas elines Ġand ĠSan ity ĠChecks . </s> When Ġcreating Ġsynthetic Ġdatasets , Ġit Ġis Ġimportant Ġto Ġensure Ġthat Ġsystematic Ġbiases , Ġor Ġannotation Ġartifacts ĠB IB REF 41 , Ġare Ġnot Ġintroduced Ġinto Ġthe Ġresulting Ġprobes Ġand Ġthat Ġthe Ġtarget Ġdatasets Ġare Ġsufficiently Ġchallenging Ġ( or Ġgood , Ġin Ġthe Ġsense Ġof ĠB IB REF 42 ). ĠTo Ġtest Ġfor Ġthis , Ġwe Ġuse Ġseveral Ġof Ġthe ĠMC Q A Ġbaseline Ġmodels Ġfirst Ġintroduced Ġin ĠB IB REF 0 , Ġwhich Ġtake Ġinspiration Ġfrom Ġthe ĠL ST M - based Ġmodels Ġused Ġin ĠB IB REF 43 Ġfor ĠNL I Ġand Ġvarious Ġpartial - input Ġbas elines Ġbased Ġon Ġthese Ġmodels . </s> Following Ġthe Ġnotation Ġfrom ĠB IB REF 0 , Ġfor Ġany Ġgiven Ġsequence Ġ$ s $ Ġof Ġtokens Ġin Ġ$\ l brace Ġq ^{ ( j ) }, Ġa _{ 1 } ^{ ( j ) }, ... , a _{ N } ^{ ( j ) }\ r brace Ġ$ Ġin Ġ$ D $, Ġan Ġencoding Ġof Ġ$ s $ Ġis Ġgiven Ġas Ġ$ h _{ s } ^{ ( j )} Ġ= Ġ\ text bf Ġ{ Bi L ST M } (\ text sc Ġ{ embed } ( s )) Ġ\ in Ġ\ math bb Ġ{ R } ^{ | s | Ġ\ times Ġ2 h } $ Ġ( where Ġ$ h $ Ġis Ġthe Ġdimension Ġof Ġthe Ġhidden Ġstate Ġin Ġeach Ġdirectional Ġnetwork , Ġand Ġembed $ (\ cd ot Ġ) $ Ġis Ġan Ġembed ding Ġfunction Ġthat Ġassigns Ġtoken - level Ġembed d ings Ġto Ġeach Ġtoken Ġin Ġ$ s $ ). ĠA Ġcontextual Ġrepresentation Ġfor Ġeach Ġ$ s $ Ġis Ġthen Ġbuilt Ġby Ġapplying Ġan Ġelement - wise Ġmax Ġoperation Ġover Ġ$ h _{ s } $ Ġas Ġfollows : </s> With Ġthese Ġcontextual Ġrepresentations , Ġdifferent Ġbaseline Ġmodels Ġcan Ġbe Ġconstructed . ĠFor Ġexample , Ġa ĠChoice - Only Ġmodel , Ġwhich Ġis Ġa Ġvariant Ġof Ġthe Ġwell - known Ġhypothesis - only Ġbaseline Ġused Ġin ĠNL I ĠB IB REF 46 , Ġscores Ġeach Ġchoice Ġ$ c _{ i } $ Ġin Ġthe Ġfollowing Ġway : </s> for Ġ$\ text bf Ġ{ W } ^{ T } Ġ\ in Ġ\ math bb Ġ{ R } ^{ 2 h } $ Ġindependently Ġof Ġthe Ġquestion Ġand Ġassigns Ġa Ġprobability Ġto Ġeach Ġanswer Ġ$ p _{ i } ^{ ( j )} Ġ\ pro pt o Ġe ^ {\ alpha Ġ_ { i } ^{ ( j ) }} $. </s> A Ġslight Ġvariant Ġof Ġthis Ġmodel , Ġthe ĠChoice - to - choice Ġmodel , Ġtries Ġto Ġsingle Ġout Ġa Ġgiven Ġanswer Ġchoice Ġrelative Ġto Ġother Ġchoices Ġby Ġscoring Ġall Ġchoice Ġpairs Ġ$\ alpha Ġ_ { i , i ^ {\ prime Ġ}} ^{ ( j )} Ġ= Ġ\ text sc Ġ{ Att } ( r ^{ ( j )} _{ c _{ i } }, r ^{ ( j )} _{ c _{ i ^ {\ prime Ġ} }} ) Ġ\ in Ġ\ math bb Ġ{ R } $ Ġusing Ġa Ġlearned Ġattention Ġmechanism ĠAtt Ġand Ġfinding Ġthe Ġchoice Ġwith Ġthe Ġminimal Ġsimilarity Ġto Ġother Ġoptions Ġ( for Ġfull Ġdetails , Ġsee Ġtheir Ġoriginal Ġpaper ). ĠIn Ġusing Ġthese Ġpartial - input Ġbas elines , Ġwhich Ġwe Ġtrain Ġdirectly Ġon Ġeach Ġtarget Ġprobe , Ġwe Ġcan Ġcheck Ġwhether Ġsystematic Ġbiases Ġrelated Ġto Ġanswer Ġchoices Ġwere Ġintroduced Ġinto Ġthe Ġdata Ġcreation Ġprocess . </s> A ĠQuestion - to - choice Ġmodel , Ġin Ġcontrast , Ġuses Ġthe Ġcontextual Ġrepresentations Ġfor Ġeach Ġquestion Ġand Ġindividual Ġchoice Ġand Ġan Ġattention Ġmodel ĠAtt Ġmodel Ġto Ġget Ġa Ġscore Ġ$\ alpha Ġ^ { ( j )} _{ q , i } Ġ= Ġ\ text sc Ġ{ Att } ( r ^{ ( j )} _{ q }, r ^{ ( j )} _{ c _{ i }} ) Ġ\ in Ġ\ math bb Ġ{ R } $ Ġas Ġabove . ĠHere Ġwe Ġalso Ġexperiment Ġwith Ġusing ĠES IM ĠB IB REF 47 Ġto Ġgenerate Ġthe Ġcontextual Ġrepresentations Ġ$ r $, Ġas Ġwell Ġas Ġa Ġsimpler ĠVec Similar ity Ġmodel Ġthat Ġmeasures Ġthe Ġaverage Ġvector Ġsimilarity Ġbetween Ġquestion Ġand Ġanswer Ġtokens : Ġ$\ alpha Ġ^ { ( j )} _{ q , i } Ġ= Ġ\ text sc Ġ{ Sim } (\ text sc Ġ{ embed } ( q ^{ ( j )} ), \ text sc Ġ{ embed } ( c ^{ ( j )} _{ i } )) $. ĠIn Ġcontrast Ġto Ġthe Ġmodels Ġabove , Ġthese Ġsets Ġof Ġbas elines Ġare Ġused Ġto Ġcheck Ġfor Ġartifacts Ġbetween Ġquestions Ġand Ġanswers Ġthat Ġare Ġnot Ġcaptured Ġin Ġthe Ġpartial - input Ġbas elines Ġ( see Ġdiscussion Ġin ĠB IB REF 49 ) Ġand Ġensure Ġthat Ġthe Ġoverall ĠMC Q A Ġtasks Ġare Ġsufficiently Ġdifficult Ġfor Ġour Ġtransformer Ġmodels . </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠIn oc ulation Ġand ĠPre - training </s> Using Ġthe Ġvarious Ġmodels Ġintroduced Ġabove , Ġwe Ġtrain Ġthese Ġmodels Ġon Ġbenchmark Ġtasks Ġin Ġthe Ġscience Ġdomain Ġand Ġlook Ġat Ġmodel Ġperformance Ġon Ġour Ġprobes Ġwith Ġand Ġwithout Ġadditional Ġtraining Ġon Ġsamples Ġof Ġprobe Ġdata , Ġbuilding Ġon Ġthe Ġidea Ġof Ġinoc ulation Ġfrom ĠB IB REF 23 . ĠModel Ġinoc ulation Ġis Ġthe Ġidea Ġof Ġcontinuing Ġto Ġtrain Ġmodels Ġon Ġnew Ġchallenge Ġtasks Ġ( in Ġour Ġcases , Ġseparately Ġfor Ġeach Ġprobe ) Ġusing Ġonly Ġa Ġsmall Ġamount Ġof Ġexamples . ĠUnlike Ġin Ġordinary Ġfine - tun ing , Ġthe Ġgoal Ġis Ġnot Ġto Ġlearn Ġan Ġentirely Ġre - pur posed Ġmodel , Ġbut Ġto Ġimprove Ġon Ġ( or Ġvacc inate Ġagainst ) Ġparticular Ġphenomena Ġ( e . g ., Ġour Ġsynthetic Ġprobes ) Ġthat Ġpotentially Ġdev iate Ġfrom Ġa Ġmodel 's Ġoriginal Ġtraining Ġdistribution Ġ( but Ġthat Ġnonetheless Ġmight Ġinvolve Ġknowledge Ġalready Ġcontained Ġin Ġthe Ġmodel ). </s> In Ġthe Ġvariant Ġproposed Ġin ĠB IB REF 22 , Ġfor Ġeach Ġpre - trained Ġ( science ) Ġmodel Ġand Ġarchitecture Ġ$ M _{ a } $ Ġwe Ġcontinue Ġtraining Ġthe Ġmodel Ġon Ġ$ k $ Ġnew Ġprobe Ġexamples Ġ( with Ġa Ġmaximum Ġof Ġ$ k =$ Ġ3 k ) Ġunder Ġa Ġset Ġof Ġdifferent Ġhyper - param eter Ġconfigurations Ġ$ j Ġ\ in Ġ\ l brace Ġ1 , Ġ... , ĠJ \ r brace Ġ$ Ġand Ġidentify , Ġfor Ġeach Ġ$ k $, Ġthe Ġmodel Ġ$ M _{ * } ^{ a , k } $ Ġwith Ġthe Ġbest Ġaggregate Ġperformance Ġ$ S $ Ġon Ġthe Ġoriginal Ġ( orig ) Ġand Ġnew Ġtask : </s> As Ġin ĠB IB REF 22 , Ġwe Ġfound Ġall Ġmodels Ġto Ġbe Ġespecially Ġsensitive Ġto Ġdifferent Ġlearning Ġrates , Ġand Ġperformed Ġcomprehensive Ġhyper - param eters Ġsearches Ġthat Ġalso Ġmanipulate Ġthe Ġnumber Ġof Ġiterations Ġand Ġrandom Ġseeds Ġused . </s> Using Ġthis Ġmethodology , Ġwe Ġcan Ġsee Ġhow Ġmuch Ġexposure Ġto Ġnew Ġdata Ġit Ġtakes Ġfor Ġa Ġgiven Ġmodel Ġto Ġmaster Ġa Ġnew Ġtask , Ġand Ġwhether Ġthere Ġare Ġphenomena Ġthat Ġstress Ġparticular Ġmodels Ġ( e . g ., Ġlead Ġto Ġcatastrophic Ġforgetting Ġof Ġthe Ġoriginal Ġtask ). ĠGiven Ġthe Ġrestrictions Ġon Ġthe Ġnumber Ġof Ġfine - tun ing Ġexamples , Ġour Ġassumption Ġis Ġthat Ġwhen Ġmodels Ġare Ġable Ġto Ġmaintain Ġgood Ġperformance Ġon Ġtheir Ġoriginal Ġtask Ġduring Ġinoc ulation , Ġthe Ġquick ness Ġwith Ġwhich Ġthey Ġare Ġable Ġto Ġlearn Ġthe Ġinoc ulated Ġtask Ġprovides Ġevidence Ġof Ġprior Ġcompetence , Ġwhich Ġis Ġprecisely Ġwhat Ġwe Ġaim Ġto Ġprobe . ĠTo Ġmeasure Ġpast Ġperformance , Ġwe Ġdefine Ġa Ġmodel 's Ġinoc ulation Ġcost Ġas Ġthe Ġdifference Ġin Ġthe Ġperformance Ġof Ġthis Ġmodel Ġon Ġits Ġoriginal Ġtask Ġbefore Ġand Ġafter Ġinoc ulation . </s> We Ġpre - train Ġon Ġan Ġaggreg ated Ġtraining Ġset Ġof Ġthe Ġbenchmark Ġscience Ġexams Ġdetailed Ġin ĠTable ĠT AB REF 21 , Ġand Ġcreated Ġan Ġaggregate Ġdevelopment Ġset Ġof Ġaround Ġ4 k Ġscience Ġquestions Ġfor Ġevaluating Ġoverall Ġscience Ġperformance Ġand Ġinoc ulation Ġcosts . ĠTo Ġhandle Ġthe Ġmismatch Ġbetween Ġnumber Ġof Ġanswer Ġchoices Ġin Ġthese Ġsets , Ġwe Ġmade Ġall Ġsets Ġ5 - way Ġby Ġadding Ġempty Ġanswers Ġas Ġneeded . ĠWe Ġalso Ġexperimented Ġwith Ġa Ġslight Ġvariant Ġof Ġinoc ulation , Ġcalled Ġadd - some Ġinoc ulation , Ġwhich Ġinvolves Ġbalancing Ġthe Ġinoc ulation Ġtraining Ġsets Ġwith Ġnatural istic Ġscience Ġquestions . ĠWe Ġreserve Ġthe ĠMC QL Ġdataset Ġin ĠTable ĠT AB REF 21 Ġfor Ġthis Ġpurpose , Ġand Ġexperiment Ġwith Ġbalancing Ġeach Ġprobe Ġexample Ġwith Ġa Ġscience Ġexample Ġ( x 1 Ġmatching ) Ġand Ġadding Ġtwice Ġas Ġmany Ġscience Ġquestions Ġ( x 2 Ġmatching , Ġup Ġto Ġ3 k ) Ġfor Ġeach Ġnew Ġexample . </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠEval uating ĠModel ĠCompet ence </s> The Ġstandard Ġway Ġto Ġevaluate Ġour ĠMC Q A Ġmodels Ġis Ġby Ġlooking Ġat Ġthe Ġoverall Ġaccuracy Ġof Ġthe Ġcorrect Ġanswer Ġprediction , Ġor Ġwhat Ġwe Ġcall Ġinstance - level Ġaccuracy Ġ( as Ġin ĠTable ĠT AB REF 25 ). ĠGiven Ġthe Ġnature Ġof Ġour Ġdata Ġand Ġthe Ġexistence Ġof Ġsemantic Ġclusters Ġas Ġdetailed Ġin ĠSection ĠSEC REF 11 Ġ( i . e ., Ġsets Ġof Ġquestions Ġand Ġanswers Ġunder Ġdifferent Ġdistract or Ġchoices Ġand Ġinference Ġcomplexity ), Ġwe Ġalso Ġmeasure Ġa Ġmodel 's Ġcluster - level Ġ( or Ġstrict Ġcluster ) Ġaccuracy , Ġwhich Ġrequires Ġcorrectly Ġanswering Ġall Ġquestions Ġin Ġa Ġcluster . ĠExample Ġsemantic Ġclusters Ġare Ġshown Ġin ĠTable ĠT AB REF 30 ; Ġin Ġthe Ġfirst Ġcase , Ġthere Ġare Ġ6 ĠIS A $ ^ \ up arrow Ġ$ Ġquestions Ġ( including Ġpert urb ations ) Ġabout Ġthe Ġconcept Ġtrou ser . n . 01 Ġ( e . g ., Ġinvolving Ġknowing Ġthat Ġtrousers Ġare Ġa Ġtype Ġof Ġconsumer Ġgood Ġand Ġgarment / cl othing ), Ġwhich Ġa Ġmodel Ġmust Ġanswer Ġin Ġorder Ġto Ġreceive Ġfull Ġcredit . </s> Our Ġcluster - based Ġanalysis Ġis Ġmotivated Ġby Ġthe Ġidea Ġthat Ġif Ġa Ġmodel Ġtruly Ġknows Ġthe Ġmeaning Ġof Ġa Ġgiven Ġconcept , Ġsuch Ġas Ġthe Ġconcept Ġof Ġtrousers , Ġthen Ġit Ġshould Ġbe Ġable Ġto Ġanswer Ġarbitrary Ġquestions Ġabout Ġthis Ġconcept Ġwithout Ġsensitivity Ġto Ġvaried Ġdistract ors . ĠWhile Ġour Ġstrict Ġcluster Ġmetric Ġis Ġsimplistic , Ġit Ġtakes Ġinspiration Ġfrom Ġwork Ġon Ġvisual ĠQ A ĠB IB REF 53 , Ġand Ġallows Ġus Ġto Ġevaluate Ġhow Ġconsistent Ġand Ġrobust Ġmodels Ġare Ġacross Ġour Ġdifferent Ġprobes , Ġand Ġto Ġget Ġinsight Ġinto Ġwhether Ġerrors Ġare Ġconcentrated Ġon Ġa Ġsmall Ġset Ġof Ġconcepts Ġor Ġwidespread Ġacross Ġclusters . </s> Results Ġand ĠFind ings </s> In Ġthis Ġsection , Ġwe Ġprovide Ġthe Ġresults Ġof Ġthe Ġempirical Ġquestions Ġfirst Ġintroduced Ġin ĠFigure ĠFIG REF 1 , Ġstarting Ġwith Ġthe Ġresults Ġof Ġour Ġbaseline Ġmodels . </s> Results Ġand ĠFind ings Ġ: :: ĠAre Ġour ĠPro bes ĠS ufficient ly ĠChall eng ing ? </s> As Ġshown Ġin ĠTable ĠT AB REF 25 , Ġmost Ġof Ġour Ġpartial - input Ġbas elines Ġ( i . e ., ĠChoice - Only Ġand ĠChoice - to - Choice Ġmodels ) Ġfailed Ġto Ġperform Ġwell Ġon Ġour Ġdataset Ġprobes Ġacross Ġa Ġwide Ġrange Ġof Ġmodels , Ġshowing Ġthat Ġsuch Ġprobes Ġare Ġgenerally Ġimmune Ġfrom Ġbiases Ġrelating Ġto Ġhow Ġdistract ors Ġwere Ġgenerated . ĠAs Ġalready Ġdiscussed Ġin ĠSection ĠSEC REF 13 , Ġhowever , Ġinitial Ġversions Ġof Ġthe ĠDictionary Q A Ġdataset Ġhad Ġunforeseen Ġbiases Ġpartly Ġrelated Ġto Ġwhether Ġdistract ors Ġwere Ġsampled Ġfrom Ġentries Ġwithout Ġexample Ġsentences , Ġwhich Ġresulted Ġin Ġhigh ĠChoice - Only - G lo Ve Ġscores Ġranging Ġaround Ġ56 % Ġaccuracy Ġbefore Ġa Ġfiltering Ġstep Ġwas Ġapplied Ġto Ġremove Ġthese Ġdistract ors . </s> We Ġhad Ġsimilar Ġissues Ġwith Ġthe Ġhyper ny my Ġprobe Ġwhich , Ġeven Ġafter Ġa Ġfiltering Ġstep Ġthat Ġused Ġour ĠChoice - to - Choice - G lo Ve Ġmodel , Ġstill Ġleads Ġto Ġhigh Ġresults Ġon Ġthe ĠB ERT Ġand ĠRo BER Ta Ġchoice - only Ġmodels . ĠGiven Ġthat Ġseveral Ġattempts Ġwere Ġmade Ġto Ġentirely Ġde - du pl icate Ġthe Ġdifferent Ġsplits Ġ( both Ġin Ġterms Ġof Ġgold Ġanswers Ġand Ġdistract or Ġtypes ), Ġthe Ġsource Ġof Ġthese Ġbiases Ġis Ġnot Ġat Ġall Ġobvious , Ġwhich Ġshows Ġhow Ġeasy Ġit Ġis Ġfor Ġunintended Ġbiases Ġin Ġexpert Ġknowledge Ġto Ġappear Ġin Ġthe Ġresulting Ġdatasets Ġand Ġthe Ġimportance Ġof Ġhaving Ġrigorous Ġbas elines . ĠWe Ġalso Ġnote Ġthe Ġlarge Ġgap Ġin Ġsome Ġcases Ġbetween Ġthe ĠB ERT Ġand ĠRo BER Ta Ġversus ĠGl o Ve Ġchoice - only Ġmodels , Ġwhich Ġhighlights Ġthe Ġneed Ġfor Ġhaving Ġpartial - input Ġbas elines Ġthat Ġuse Ġthe Ġbest Ġavailable Ġmodels . </s> Using Ġa Ġmore Ġconventional Ġset Ġof ĠTask - Specific ĠQ A Ġmodels Ġ( i . e ., Ġthe ĠL ST M - based ĠQuestion - to - Choice Ġmodels Ġtrained Ġdirectly Ġon Ġthe Ġprobes ), Ġwe Ġcan Ġsee Ġthat Ġresults Ġare Ġnot Ġparticularly Ġstrong Ġon Ġany Ġof Ġthe Ġdatasets , Ġsuggesting Ġthat Ġour Ġprobes Ġare Ġindeed Ġsufficiently Ġchallenging Ġand Ġlargely Ġimmune Ġfrom Ġovert Ġartifacts . ĠThe Ġpoor Ġperformance Ġof Ġthe ĠVec Similar ity Ġ( which Ġuses Ġpre - trained ĠWord 2 V ec Ġembed d ings Ġwithout Ġadditional Ġtraining ) Ġprovides Ġadditional Ġevidence Ġthat Ġelementary Ġlex ical Ġmatching Ġstrategies Ġare Ġinsufficient Ġfor Ġsolving Ġany Ġof Ġthe Ġprobing Ġtasks . </s> Results Ġand ĠFind ings Ġ: :: ĠHow Ġwell Ġdo Ġpre - trained ĠMC Q A Ġmodels Ġdo ? </s> Science Ġmodels Ġthat Ġuse Ġnon - trans former Ġbased Ġenc od ers , Ġsuch Ġas Ġthe ĠES IM Ġmodel Ġwith ĠGl o Ve Ġand ĠEL MO , Ġperform Ġpoorly Ġacross Ġall Ġprobes , Ġin Ġmany Ġcases Ġscoring Ġnear Ġrandom Ġchance , Ġshowing Ġlimits Ġto Ġhow Ġwell Ġthey Ġgeneral ize Ġfrom Ġscience Ġto Ġother Ġtasks Ġeven Ġwith Ġpre - trained ĠGl o Ve Ġand ĠEL MO Ġembed d ings . ĠIn Ġsharp Ġcontrast , Ġthe Ġtransformer Ġmodels Ġhave Ġmixed Ġresults , Ġthe Ġmost Ġstriking Ġresult Ġbeing Ġthe ĠRo BER Ta Ġmodels Ġon Ġthe Ġdefinitions Ġand Ġsyn ony my Ġprobes Ġ( ach ieving Ġa Ġtest Ġaccuracy Ġof Ġ77 % Ġand Ġ61 %, Ġrespectively ), Ġwhich Ġoutper form Ġseveral Ġof Ġthe Ġtask - specific ĠL ST M Ġmodels Ġtrained Ġdirectly Ġon Ġthe Ġprobes . ĠAt Ġfirst Ġglance , Ġthis Ġsuggests Ġthat ĠRo BER Ta , Ġwhich Ġgenerally Ġfar Ġoutp aces Ġeven ĠB ERT Ġacross Ġmost Ġprobes , Ġhas Ġhigh Ġcompetence Ġof Ġdefinitions Ġand Ġsyn onyms Ġeven Ġwithout Ġexplicit Ġtraining Ġon Ġour Ġnew Ġtasks . </s> Given Ġthe Ġcontrolled Ġnature Ġof Ġour Ġprobes , Ġwe Ġcan Ġget Ġa Ġmore Ġdetailed Ġview Ġof Ġhow Ġwell Ġthe Ġscience Ġmodels Ġare Ġperforming Ġacross Ġdifferent Ġreasoning Ġand Ġdistract or Ġtypes , Ġas Ġshown Ġin Ġthe Ġfirst Ġcolumn Ġof ĠFigure ĠFIG REF 28 Ġfor ĠES IM Ġand ĠRo BER Ta . ĠThe ĠES IM Ġscience Ġmodel Ġwithout Ġtraining Ġhas Ġuniformly Ġpoor Ġperformance Ġacross Ġall Ġcategories , Ġwhereas Ġthe Ġperformance Ġof ĠRo BER Ta Ġis Ġmore Ġvaried . ĠAcross Ġall Ġdatasets Ġand Ġnumber Ġof Ġhops Ġ( i . e ., Ġthe Ġrows Ġin Ġthe Ġheat Ġmaps ), Ġmodel Ġperformance Ġfor ĠRo BER Ta Ġis Ġconsistently Ġhighest Ġamong Ġexamples Ġwith Ġrandom Ġdistract ors Ġ( i . e ., Ġthe Ġfirst Ġcolumn ), Ġand Ġlowest Ġin Ġcases Ġinvolving Ġdistract ors Ġthat Ġare Ġclosest Ġin ĠWord Net Ġspace Ġ( e . g ., Ġsister Ġand ĠIS A , Ġor Ġup / down , Ġdistract ors Ġof Ġdistance Ġ$ k ^ {\ prime Ġ} = 1 $ ). ĠThis Ġis Ġnot Ġsurprising , Ġgiven Ġthat , Ġin Ġthe Ġfirst Ġcase , Ġrandom Ġdistract ors Ġare Ġlikely Ġto Ġbe Ġthe Ġeasiest Ġcategory Ġ( and Ġthe Ġopposite Ġfor Ġdistract ors Ġclose Ġin Ġspace ), Ġbut Ġsuggests Ġthat ĠRo BER Ta Ġmight Ġonly Ġbe Ġgetting Ġthe Ġeasiest Ġcases Ġcorrect . </s> Model Ġperformance Ġalso Ġclearly Ġde grades Ġfor Ġhyper ny my Ġand Ġhyp ony my Ġacross Ġall Ġmodels Ġas Ġthe Ġnumber Ġof Ġhops Ġ$ k $ Ġincreases Ġ( see Ġred Ġdashed Ġboxes ). ĠFor Ġexample , Ġproblems Ġthat Ġinvolve Ġhyp onym Ġreasoning Ġwith Ġsister Ġdistract ors Ġof Ġdistance Ġ$ k ^ {\ prime Ġ} = 1 $ Ġ( i . e ., Ġthe Ġsecond Ġcolumn ) Ġde grades Ġfrom Ġ47 % Ġto Ġ15 % Ġwhen Ġthe Ġnumber Ġof Ġhops Ġ$ k $ Ġincreases Ġfrom Ġ1 Ġto Ġ4 . ĠThis Ġgeneral Ġtendency Ġpersists Ġeven Ġafter Ġadditional Ġfine - tun ing , Ġas Ġwe Ġdiscuss Ġnext , Ġand Ġgives Ġevidence Ġthat Ġmodels Ġare Ġlimited Ġin Ġtheir Ġcapacity Ġfor Ġcertain Ġtypes Ġof Ġmulti - hop Ġinf erences . </s> As Ġdiscussed Ġby ĠB IB REF 26 , Ġthe Ġchoice Ġof Ġgeneration Ġtemplates Ġcan Ġhave Ġa Ġsignificant Ġeffect Ġon Ġmodel Ġperformance . ĠThe Ġresults Ġso Ġfar Ġshould Ġtherefore Ġbe Ġregarded Ġas Ġa Ġlower Ġbound Ġon Ġmodel Ġcompetence . ĠIt Ġis Ġpossible Ġthat Ġmodel Ġperformance Ġis Ġhigh Ġfor Ġdefinitions , Ġfor Ġexample , Ġbecause Ġthe Ġassociated Ġtemplates Ġbest Ġalign Ġwith Ġthe Ġscience Ġtraining Ġdistribution Ġ( which Ġwe Ġknow Ġlittle Ġabout ). ĠFor Ġthis Ġreason , Ġthe Ġsubsequent Ġinoc ulation Ġstep Ġis Ġimportant âĢĶ it Ġgives Ġthe Ġmodel Ġan Ġopportunity Ġto Ġlearn Ġabout Ġour Ġtarget Ġtemplates Ġand Ġcouple Ġthis Ġlearned Ġknowledge Ġwith Ġits Ġgeneral Ġknowledge Ġacquired Ġduring Ġpre - training Ġand Ġscience Ġtraining Ġ( which Ġis , Ġagain , Ġwhat Ġwe Ġaim Ġto Ġprobe ). </s> Results Ġand ĠFind ings Ġ: :: ĠCan ĠModels ĠBe ĠEffect ively ĠIn oc ulated ? </s> Model Ġperformance Ġafter Ġadditional Ġfine - tun ing , Ġor Ġinoc ulation , Ġis Ġshown Ġin Ġthe Ġlast Ġ3 Ġrows Ġof ĠTable ĠT AB REF 25 , Ġalong Ġwith Ġlearning Ġcurves Ġshown Ġin ĠFigure ĠFIG REF 29 Ġfor Ġa Ġselection Ġof Ġprobes Ġand Ġmodels . ĠIn Ġthe Ġformer Ġcase , Ġthe Ġperformance Ġrepresents Ġthe Ġmodel Ġ( and Ġinoc ulation Ġamount ) Ġwith Ġthe Ġhighest Ġaggregate Ġperformance Ġover Ġthe Ġold Ġtask Ġand Ġnew Ġprobe . ĠHere Ġwe Ġagain Ġsee Ġthe Ġtransformer - based Ġmodels Ġoutper form Ġnon - trans former Ġmodels , Ġand Ġthat Ġbetter Ġmodels Ġcorrelate Ġwith Ġlower Ġinoc ulation Ġcosts . ĠFor Ġexample , Ġwhen Ġinoc ulating Ġon Ġsyn ony my , Ġthe Ġcost Ġfor ĠES IM Ġis Ġaround Ġ7 % Ġreduced Ġaccuracy Ġon Ġits Ġoriginal Ġtask , Ġas Ġopposed Ġto Ġ$ < Ġ1 $ % Ġand Ġaround Ġ1 % Ġfor ĠB ERT Ġand ĠRo BER Ta , Ġrespectively . ĠThis Ġshows Ġthe Ġhigh Ġcapacity Ġfor Ġtransformer Ġmodels Ġto Ġabsorb Ġnew Ġtasks Ġwith Ġminimal Ġcosts , Ġas Ġalso Ġobserved Ġin ĠB IB REF 22 Ġfor ĠNL I . </s> As Ġshown Ġin ĠFigure ĠFIG REF 29 , Ġtransformer Ġmodels Ġtend Ġto Ġlearn Ġmost Ġtasks Ġfairly Ġquickly Ġwhile Ġkeeping Ġconstant Ġscores Ġon Ġtheir Ġoriginal Ġtasks Ġ( i . e ., Ġthe Ġflat Ġdashed Ġlines Ġobserved Ġin Ġplots Ġ1 - 4 ), Ġwhich Ġgives Ġevidence Ġof Ġhigh Ġcompetence . ĠIn Ġboth Ġcases , Ġadd - some Ġinoc ulation Ġproves Ġto Ġbe Ġa Ġcheap Ġand Ġeasy Ġway Ġto Ġ1 ) Ġimprove Ġscores Ġon Ġthe Ġprobing Ġtasks Ġ( i . e ., Ġthe Ġsolid Ġblack Ġand Ġblue Ġlines Ġin Ġplot Ġ1 ) Ġand ; Ġ2 ) Ġminimize Ġloss Ġon Ġscience Ġ( e . g ., Ġthe Ġblue Ġand Ġblack Ġdashed Ġlines Ġin Ġplots Ġ2 - 4 ). ĠThe Ġopposite Ġis Ġthe Ġcase Ġfor ĠES IM Ġ( pl ots Ġ5 - 6 ); Ġmodels Ġare Ġgenerally Ġunable Ġto Ġsimultaneously Ġlearn Ġindividual Ġprobes Ġwithout Ġdegrading Ġon Ġtheir Ġoriginal Ġtask , Ġand Ġadding Ġmore Ġscience Ġdata Ġduring Ġinoc ulation Ġconf uses Ġmodels Ġon Ġboth Ġtasks . </s> As Ġshown Ġin ĠFigure ĠFIG REF 28 , ĠRo BER Ta Ġis Ġable Ġto Ġsignificantly Ġimprove Ġperformance Ġacross Ġmost Ġcategories Ġeven Ġafter Ġinoc ulation Ġwith Ġa Ġmere Ġ100 Ġexamples Ġ( the Ġmiddle Ġplot ), Ġwhich Ġagain Ġprovides Ġstrong Ġevidence Ġof Ġprior Ġcompetence . ĠAs Ġan Ġexample , ĠRo BER Ta Ġimproves Ġon Ġ2 - hop Ġhyp ony my Ġinference Ġwith Ġrandom Ġdistract ors Ġby Ġ18 % Ġ( from Ġ59 % Ġto Ġ77 %). ĠAfter Ġ3 k Ġexamples , Ġthe Ġmodel Ġhas Ġhigh Ġperformance Ġon Ġvirtually Ġall Ġcategories Ġ( the Ġsame Ġscore Ġincreases Ġfrom Ġ59 % Ġto Ġ87 %), Ġhowever Ġresults Ġstill Ġtends Ġto Ġdegrade Ġas Ġa Ġfunction Ġof Ġhop Ġand Ġdistract or Ġcomplexity , Ġas Ġdiscussed Ġabove . </s> Despite Ġthe Ġhigh Ġperformance Ġof Ġour Ġtransformer Ġmodels Ġafter Ġinoc ulation , Ġmodel Ġperformance Ġon Ġmost Ġprobes Ġ( with Ġthe Ġexception Ġof ĠDefinitions ) Ġaverages Ġaround Ġ80 % Ġfor Ġour Ġbest Ġmodels . ĠThis Ġsuggests Ġthat Ġthere Ġis Ġstill Ġconsiderable Ġroom Ġfor Ġimprovement , Ġespecially Ġfor Ġsyn ony my Ġand Ġword Ġsense , Ġwhich Ġis Ġa Ġtopic Ġthat Ġwe Ġdiscuss Ġmore Ġin ĠSection ĠSEC REF 6 . </s> Results Ġand ĠFind ings Ġ: :: ĠAre ĠModels ĠCons istent Ġacross ĠCl usters ? </s> Table ĠT AB REF 32 Ġshows Ġcluster - level Ġaccur acies Ġfor Ġthe Ġdifferent ĠWord Net Q A Ġprobes . ĠAs Ġwith Ġperformance Ġacross Ġthe Ġdifferent Ġinference / dist ractor Ġcategories , Ġthese Ġresults Ġare Ġmixed . ĠFor Ġsome Ġprobes , Ġsuch Ġas Ġdefinitions , Ġour Ġbest Ġmodels Ġappear Ġto Ġbe Ġrather Ġrobust ; Ġe . g ., Ġour ĠRo BER Ta Ġmodel Ġhas Ġa Ġcluster Ġaccuracy Ġof Ġ$ 75 \ % $, Ġmeaning Ġthat Ġit Ġcan Ġanswer Ġall Ġquestions Ġperfectly Ġfor Ġ75 % Ġof Ġthe Ġtarget Ġconcepts Ġand Ġthat Ġerrors Ġare Ġconcentrated Ġon Ġa Ġsmall Ġminority Ġ( 25 %) Ġof Ġconcepts . ĠOn Ġsyn ony my Ġand Ġhyper ny my , Ġboth ĠB ERT Ġand ĠRo BER Ta Ġappear Ġrobust Ġon Ġthe Ġmajority Ġof Ġconcepts , Ġshowing Ġthat Ġerrors Ġare Ġsimilarly Ġconcentrated . ĠIn Ġcontrast , Ġour Ġbest Ġmodel Ġon Ġhyp ony my Ġhas Ġan Ġaccuracy Ġof Ġ36 %, Ġmeaning Ġthat Ġits Ġerrors Ġare Ġspread Ġacross Ġmany Ġconcepts , Ġthus Ġsuggesting Ġless Ġrobust ness . </s> Table ĠT AB REF 30 Ġshows Ġa Ġselection Ġof Ġsemantic Ġclusters Ġinvolving ĠIS A Ġreasoning , Ġas Ġwell Ġas Ġthe Ġmodel Ġperformance Ġover Ġdifferent Ġanswers Ġ( shown Ġsymbol ically ) Ġand Ġpert urb ations . ĠFor Ġexample , Ġin Ġthe Ġthe Ġsecond Ġcase , Ġthe Ġcluster Ġis Ġbased Ġaround Ġthe Ġconcept / syn set Ġoppose . v . 06 Ġand Ġinvolves Ġ4 Ġinf erences Ġand Ġa Ġtotal Ġ24 Ġquestions Ġ( i . e ., Ġinf erences Ġwith Ġpert urb ations ). ĠOur Ġweakest Ġmodel , ĠES IM , Ġanswers Ġonly Ġ5 Ġout Ġof Ġ24 Ġquestions Ġcorrectly , Ġwhereas ĠRo BER Ta Ġgets Ġ21 / 24 . ĠIn Ġthe Ġother Ġcases , ĠRo BER Ta Ġgets Ġall Ġclusters Ġcorrect , Ġwhereas ĠB ERT Ġand ĠES IM Ġget Ġnone Ġof Ġthem Ġcorrect . </s> We Ġemphasize Ġthat Ġthese Ġresults Ġonly Ġprovide Ġa Ġcrude Ġlook Ġinto Ġmodel Ġconsistency Ġand Ġrobust ness . ĠRec alling Ġagain Ġthe Ġdetails Ġin ĠTable ĠT AB REF 12 , Ġprobes Ġdiffer Ġin Ġterms Ġof Ġaverage Ġsize Ġof Ġclusters . ĠHyp ony my , Ġin Ġvirtue Ġof Ġhaving Ġmany Ġmore Ġquestions Ġper Ġcluster , Ġmight Ġsimply Ġbe Ġa Ġmuch Ġmore Ġdifficult Ġdataset . ĠIn Ġaddition , Ġsuch Ġa Ġstrict Ġevaluation Ġdoes Ġnot Ġtake Ġinto Ġaccount Ġpotential Ġerrors Ġinside Ġof Ġclusters , Ġwhich Ġis Ġan Ġimportant Ġissue Ġthat Ġwe Ġdiscuss Ġin Ġthe Ġnext Ġsection . ĠWe Ġleave Ġaddressing Ġsuch Ġissues Ġand Ġcoming Ġup Ġwith Ġmore Ġinsightful Ġcluster - based Ġmetrics Ġfor Ġfuture Ġwork . </s> Discussion Ġand ĠConclusion </s> We Ġpresented Ġseveral Ġnew Ġchallenge Ġdatasets Ġand Ġa Ġnovel Ġmethodology Ġfor Ġautomatically Ġbuilding Ġsuch Ġdatasets Ġfrom Ġknowledge Ġgraphs Ġand Ġtax onom ies . ĠWe Ġused Ġthese Ġto Ġprobe Ġstate - of - the - art Ġopen - domain ĠQ A Ġmodels Ġ( cent ering Ġaround Ġmodels Ġbased Ġon Ġvariants Ġof ĠB ERT ). ĠWhile Ġour Ġgeneral Ġmethodology Ġis Ġamend able Ġto Ġany Ġtarget Ġknowledge Ġresource Ġor ĠQ A Ġmodel / domain , Ġwe Ġfocus Ġon Ġprobing Ġdefinitions Ġand ĠIS A Ġknowledge Ġusing Ġopen - source Ġdiction aries Ġand ĠMC Q A Ġmodels Ġtrained Ġin Ġthe Ġscience Ġdomain . </s> We Ġfind , Ġconsistent Ġwith Ġrecent Ġprobing Ġstudies ĠB IB REF 26 , Ġthat Ġtransformer - based Ġmodels Ġhave Ġa Ġremarkable Ġability Ġto Ġanswer Ġquestions Ġthat Ġinvolve Ġcomplex Ġforms Ġof Ġrelational Ġknowledge , Ġboth Ġwith Ġand Ġwithout Ġexplicit Ġexposure Ġto Ġour Ġnew Ġtarget Ġtasks . ĠIn Ġthe Ġlatter Ġcase , Ġa Ġnewer ĠRo BER Ta Ġmodel Ġtrained Ġonly Ġon Ġbenchmark Ġscience Ġtasks Ġis Ġable Ġto Ġoutper form Ġseveral Ġtask - specific ĠL ST M - based Ġmodels Ġtrained Ġdirectly Ġon Ġour Ġprobing Ġdata . ĠWhen Ġre - trained Ġon Ġsmall Ġsamples Ġ( e . g ., Ġ100 Ġexamples ) Ġof Ġprobing Ġdata Ġusing Ġvariations Ġof Ġthe Ġloss less Ġinoc ulation Ġstrategy Ġfrom ĠB IB REF 22 , ĠRo BER Ta Ġis Ġable Ġto Ġmaster Ġmany Ġaspects Ġof Ġour Ġprobes Ġwith Ġvirtually Ġno Ġperformance Ġloss Ġon Ġits Ġoriginal ĠQ A Ġtask . </s> These Ġpositive Ġresults Ġsuggest Ġthat Ġtransformer - based Ġmodels , Ġespecially Ġmodels Ġadditionally Ġfine - tun ed Ġon Ġsmall Ġsamples Ġof Ġsynthetic Ġdata , Ġcan Ġbe Ġused Ġin Ġplace Ġof Ġtask - specific Ġmodels Ġused Ġfor Ġquer ying Ġrelational Ġknowledge , Ġas Ġhas Ġalready Ġbeen Ġdone Ġfor Ġtargeted Ġtasks Ġsuch Ġas Ġword Ġsense Ġdis amb ig uation ĠB IB REF 54 . ĠSince Ġmodels Ġseem Ġto Ġalready Ġcontain Ġconsiderable Ġamounts Ġof Ġrelational Ġknowledge , Ġour Ġsimple Ġinoc ulation Ġstrategy , Ġwhich Ġtries Ġto Ġn udge Ġmodels Ġto Ġbring Ġout Ġthis Ġknowledge Ġexplicitly , Ġcould Ġserve Ġas Ġa Ġcheaper Ġalternative Ġto Ġrecent Ġattempts Ġto Ġbuild Ġarchitectures Ġthat Ġexplicitly Ġincorporate Ġstructured Ġknowledge ĠB IB REF 55 ; Ġwe Ġsee Ġmany Ġareas Ġwhere Ġour Ġinoc ulation Ġstrategy Ġcould Ġbe Ġimproved Ġfor Ġsuch Ġpurposes , Ġincluding Ġhaving Ġmore Ġcomplex Ġloss Ġfunctions Ġthat Ġmanage Ġold Ġand Ġnew Ġinformation , Ġas Ġwell Ġas Ġusing Ġtechniques Ġthat Ġtake Ġinto Ġaccount Ġnetwork Ġplastic ity ĠB IB REF 56 . </s> The Ġmain Ġappeal Ġof Ġusing Ġautomatically Ġgenerate Ġdatasets Ġis Ġthe Ġability Ġto Ġsystematically Ġmanipulate Ġand Ġcontrol Ġthe Ġcomplexity Ġof Ġtarget Ġquestions , Ġwhich Ġallows Ġfor Ġmore Ġcontrolled Ġexperimentation Ġand Ġnew Ġforms Ġof Ġevaluation . ĠDespite Ġthe Ġpositive Ġresults Ġdescribed Ġabove , Ġresults Ġthat Ġlook Ġdirectly Ġat Ġthe Ġeffect Ġof Ġdifferent Ġtypes Ġof Ġdistract ors Ġand Ġthe Ġcomplexity Ġof Ġreasoning Ġshow Ġthat Ġour Ġbest Ġmodels , Ġeven Ġafter Ġadditional Ġfine - tun ing , Ġstruggle Ġwith Ġcertain Ġcategories Ġof Ġhard Ġdistract ors Ġand Ġmulti - hop Ġinf erences . ĠFor Ġsome Ġprobes , Ġour Ġcluster - based Ġanalysis Ġalso Ġreveals Ġthat Ġerrors Ġare Ġwidespread Ġacross Ġconcept Ġclusters , Ġsuggesting Ġthat Ġmodels Ġare Ġnot Ġalways Ġconsistent Ġand Ġrobust . ĠThese Ġresults , Ġtaken Ġtogether Ġwith Ġour Ġfindings Ġabout Ġthe Ġvulnerability Ġof Ġsynthetic Ġdatasets Ġto Ġsystematic Ġbiases , Ġsuggest Ġthat Ġthere Ġis Ġmuch Ġroom Ġfor Ġimprovement Ġand Ġthat Ġthe Ġpositive Ġresults Ġshould Ġbe Ġtaken Ġwith Ġa Ġgrain Ġof Ġsalt . ĠDevelop ing Ġbetter Ġways Ġto Ġevaluate Ġsemantic Ġclusters Ġand Ġmodel Ġrobust ness Ġwould Ġbe Ġa Ġstep Ġin Ġthis Ġdirection . </s> We Ġemphasize Ġthat Ġusing Ġsynthetic Ġversus Ġnatural istic ĠQ A Ġdata Ġcomes Ġwith Ġimportant Ġtrade - offs . ĠWhile Ġwe Ġare Ġable Ġto Ġgenerate Ġlarge Ġamounts Ġof Ġsystematically Ġcontrolled Ġdata Ġat Ġvirtually Ġno Ġcost Ġor Ġneed Ġfor Ġmanual Ġannotation , Ġit Ġis Ġmuch Ġharder Ġto Ġvalidate Ġthe Ġquality Ġof Ġsuch Ġdata Ġat Ġsuch Ġa Ġscale Ġand Ġsuch Ġvarying Ġlevels Ġof Ġcomplexity . ĠConversely , Ġwith Ġbenchmark ĠQ A Ġdatasets , Ġit Ġis Ġmuch Ġharder Ġto Ġperform Ġthe Ġtype Ġof Ġcareful Ġmanip ulations Ġand Ġcluster - based Ġanalyses Ġwe Ġreport Ġhere . ĠWhile Ġwe Ġassume Ġthat Ġthe Ġexpert Ġknowledge Ġwe Ġemploy , Ġin Ġvirtue Ġof Ġbeing Ġhand - cur ated Ġby Ġhuman Ġexperts , Ġis Ġgenerally Ġcorrect , Ġwe Ġknow Ġthat Ġsuch Ġresources Ġare Ġfall ible Ġand Ġerror - prone . ĠInitial Ġcrowd - s ourcing Ġexperiments Ġthat Ġlook Ġat Ġvalid ating Ġsamples Ġof Ġour Ġdata Ġshow Ġhigh Ġagreement Ġacross Ġprobes Ġand Ġthat Ġhuman Ġscores Ġcorrelate Ġwith Ġthe Ġmodel Ġtrends Ġacross Ġthe Ġprobe Ġcategories . ĠMore Ġdetails Ġof Ġthese Ġstudies Ġare Ġleft Ġfor Ġfuture Ġwork .
Introduction Automatically answering questions, especially in the open-domain setting (i.e., where minimal or no contextual knowledge is explicitly provided), requires bringing to bear considerable amount of background knowledge and reasoning abilities. For example, knowing the answers to the two questions in Figure FIGREF1 requires identifying a specific ISA relation (i.e., that cooking is a type of learned behavior) as well as recalling the definition of a concept (i.e., that global warming is defined as a worldwide increase in temperature). In the multiple-choice setting, which is the variety of question-answering (QA) that we focus on in this paper, there is also pragmatic reasoning involved in selecting optimal answer choices (e.g., while greenhouse effect might in some other context be a reasonable answer to the second question in Figure FIGREF1, global warming is a preferable candidate). Recent successes in QA, driven largely by the creation of new resources BIBREF2, BIBREF3, BIBREF4, BIBREF5 and advances in model pre-training BIBREF6, BIBREF7, raise a natural question: do state-of-the-art multiple-choice QA (MCQA) models that excel at standard tasks really have basic knowledge and reasoning skills? Most existing MCQA datasets are constructed through either expensive crowd-sourcing BIBREF8 or hand engineering effort, in the former case making it possible to collect large amounts of data at the cost of losing systematic control over the semantics of the target questions. Hence, doing a controlled experiment to answer such a question for QA is difficult given a lack of targeted challenge datasets. Having definitive empirical evidence of model competence on any given phenomenon requires constructing a wide range of systematic tests. For example, in measuring competence of definitions, not only do we want to see that the model can handle individual questions such as Figure FIGREF1.1 inside of benchmark tasks, but that it can answer a wider range of questions that exhaustively cover a broad set of concepts and question perturbations (i.e., systematic adjustments to how the questions are constructed). The same applies to ISA reasoning; not only is it important to recognize in the question in Figure FIGREF1.1 that cooking is a learned behavior, but also that cooking is a general type of behavior or, through a few more inferential steps, a type of human activity. In this paper, we look at systematically constructing such tests by exploiting the vast amounts of structured information contained in various types of expert knowledge such as knowledge graphs and lexical taxonomies. Our general methodology works as illustrated in Figure FIGREF1: given any MCQA model trained on a set of benchmark tasks, we systematically generate a set of synthetic dataset probes (i.e., MCQA renderings of the target information) from information in expert knowledge sources. We then use these probes to ask two empirical questions: 1) how well do models trained on benchmark tasks perform on these probing tasks and; 2) can such models be re-trained to master new challenges with minimal performance loss on their original tasks? While our methodology is amenable to any knowledge source and set of models/benchmark tasks, we focus on probing state-of-the-art transformer models BIBREF7, BIBREF9 in the domain of science MCQA. For sources of expert knowledge, we use WordNet, a comprehensive lexical ontology, and other publicly available dictionary resources. We devise probes that measure model competence in definition and taxonomic knowledge in different settings (including hypernymy, hyponymy, and synonymy detection, and word sense disambiguation). This choice is motivated by fact that the science domain is considered particularly challenging for QA BIBREF10, BIBREF11, BIBREF12, and existing science benchmarks are known to involve widespread use of such knowledge (see BIBREF1, BIBREF13 for analysis), which is also arguably fundamental to more complex forms of reasoning. We show that accurately probing QA models via synthetic datasets is not straightforward, as unexpected artifacts can easily arise in such data. This motivates our carefully constructed baselines and close data inspection to ensure probe quality. Our results confirm that transformer-based QA models have a remarkable ability to recognize certain types of knowledge captured in our probes—even without additional fine-tuning. Such models can even outperform strong task-specific models trained directly on our probing tasks (e.g., on definitions, our best model achieves 77% test accuracy without specialized training, as opposed to 51% for a task-specific LSTM-based model). We also show that the same models can be effectively re-fine-tuned on small samples (even 100 examples) of probe data, and that high performance on the probes tends to correlate with a smaller drop in the model's performance on the original QA task. Our comprehensive assessment reveals several interesting nuances to the overall positive trend. For example, the performance of even the best QA models degrades substantially on our hyponym probes (by 8-15%) when going from 1-hop links to 2-hops. Further, the accuracy of even our best models on the WordNetQA probe drops by 14-44% under our cluster-based analysis, which assesses whether a model knows several facts about each individual concept, rather than just being good at answering isolated questions. State-of-the-art QA models thus have much room to improve even in some fundamental building blocks, namely definitions and taxonomic hierarchies, of more complex forms of reasoning. Related Work We follow recent work on constructing challenge datasets for probing neural models, which has primarily focused on the task of natural language inference (NLI) BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18. Most of this work looks at constructing data through adversarial generation methods, which have also been found useful for creating stronger models BIBREF19. There has also been work on using synthetic data of the type we consider in this paper BIBREF20, BIBREF21, BIBREF22. We closely follow the methodology of BIBREF22, who use hand-constructed linguistic fragments to probe NLI models and study model re-training using a variant of the inoculation by fine-tuning strategy of BIBREF23. In contrast, we focus on probing open-domain MCQA models (see BIBREF24 for a related study in the reading comprehension setting) as well as constructing data from much larger sources of structured knowledge. Our main study focuses on probing the BERT model and fine-tuning approach of BIBREF7, and other variants thereof, which are all based on the transformer architecture of BIBREF25. Related to our efforts, there have been recent studies into the types of relational knowledge contained in large-scale knowledge models BIBREF26, BIBREF27, BIBREF28, which, similar to our work, probe models using structured knowledge sources. This prior work, however, primarily focuses on unearthing the knowledge contained in the underlying language models as is without further training, using simple (single token) cloze-style probing tasks and templates (similar to what we propose in Section SECREF3). In contrast, we focus on understanding the knowledge contained in language models after they have been trained for a QA end-task using benchmark datasets in which such knowledge is expected to be widespread. Further, our evaluation is done before and after these models are fine-tuned on our probe QA tasks, using a more complex set of QA templates and target inferences. The use of lexical resources and knowledge graphs such as WordNet to construct datasets has a long history, and has recently appeared in work on adversarial attacks BIBREF14, BIBREF29 and general task construction BIBREF30, BIBREF31. In the area of MCQA, there is related work on constructing questions from tuples BIBREF32, BIBREF3, both of which involve standard crowd annotation to elicit question-answer pairs (see also BIBREF33, BIBREF34). In contrast to this work, we focus on generating data in an entirely automatic fashion, which obviates the need for expensive annotation and gives us the flexibility to construct much larger datasets that control a rich set of semantic aspects of the target questions. Dataset Probes and Construction Our probing methodology starts by constructing challenge datasets (Figure FIGREF1, yellow box) from a target set of knowledge resources. Each of our probing datasets consists of multiple-choice questions that include a question $\textbf {q}$ and a set of answer choices or candidates $\lbrace a_{1},...a_{N}\rbrace $. This section describes in detail the 5 different datasets we build, which are drawn from two sources of expert knowledge, namely WordNet BIBREF35 and the GNU Collaborative International Dictionary of English (GCIDE). We describe each resource in turn, and explain how the resulting dataset probes, which we call WordNetQA and DictionaryQA, are constructed. For convenience, we will describe each source of expert knowledge as a directed, edge-labeled graph $G$. The nodes of this graph are $\mathcal {V} = \mathcal {C} \cup \mathcal {W} \cup \mathcal {S} \cup \mathcal {D}$, where $\mathcal {C}$ is a set of atomic concepts, $\mathcal {W}$ a set of words, $\mathcal {S}$ a set of sentences, and $\mathcal {D}$ a set of definitions (see Table TABREF4 for details for WordNet and GCIDE). Each edge of $G$ is directed from an atomic concept in $\mathcal {C}$ to another node in $V$, and is labeled with a relation, such as hypernym or isa$^\uparrow $, from a set of relations $\mathcal {R}$ (see Table TABREF4). When defining our probe question templates, it will be useful to view $G$ as a set of (relation, source, target) triples $\mathcal {T} \subseteq \mathcal {R} \times \mathcal {C} \times \mathcal {V}$. Due to their origin in an expert knowledge source, such triples preserve semantic consistency. For instance, when the relation in a triple is def, the corresponding edge maps a concept in $\mathcal {C}$ to a definition in $\mathcal {D}$. To construct probe datasets, we rely on two heuristic functions, defined below for each individual probe: $\textsc {gen}_{\mathcal {Q}}(\tau )$, which generates gold question-answer pairs $(\textbf {q},\textbf {a})$ from a set of triples $\tau \subseteq \mathcal {T}$ and question templates $\mathcal {Q}$, and $\textsc {distr}(\tau ^{\prime })$, which generates distractor answers choices $\lbrace a^{\prime }_{1},...a^{\prime }_{N-1} \rbrace $ based on another set of triples $\tau ^{\prime }$ (where usually $\tau \subset \tau ^{\prime }$). For brevity, we will use $\textsc {gen}(\tau )$ to denote $\textsc {gen}_{\mathcal {Q}}(\tau )$, leaving question templates $\mathcal {Q}$ implicit. Dataset Probes and Construction ::: WordNetQA WordNet is an English lexical database consisting of around 117k concepts, which are organized into groups of synsets that each contain a gloss (i.e., a definition of the target concept), a set of representative English words (called lemmas), and, in around 33k synsets, example sentences. In addition, many synsets have ISA links to other synsets that express complex taxonomic relations. Figure FIGREF6 shows an example and Table TABREF4 summarizes how we formulate WordNet as a set of triples $\mathcal {T}$ of various types. These triples together represent a directed, edge-labeled graph $G$. Our main motivation for using WordNet, as opposed to a resource such as ConceptNet BIBREF36, is the availability of glosses ($\mathcal {D}$) and example sentences ($\mathcal {S}$), which allows us to construct natural language questions that contextualize the types of concepts we want to probe. Dataset Probes and Construction ::: WordNetQA ::: Example Generation @!START@$\textsc {gen}(\tau )$@!END@. We build 4 individual datasets based on semantic relations native to WordNet (see BIBREF37): hypernymy (i.e., generalization or ISA reasoning up a taxonomy, ISA$^\uparrow $), hyponymy (ISA$^{\downarrow }$), synonymy, and definitions. To generate a set of questions in each case, we employ a number of rule templates $\mathcal {Q}$ that operate over tuples. A subset of such templates is shown in Table TABREF8. The templates were designed to mimic naturalistic questions we observed in our science benchmarks. For example, suppose we wish to create a question $\textbf {q}$ about the definition of a target concept $c \in \mathcal {C}$. We first select a question template from $\mathcal {Q}$ that first introduces the concept $c$ and its lemma $l \in \mathcal {W}$ in context using the example sentence $s \in \mathcal {S}$, and then asks to identify the corresponding WordNet gloss $d \in \mathcal {D}$, which serves as the gold answer $\textbf {a}$. The same is done for ISA reasoning; each question about a hypernym/hyponym relation between two concepts $c \rightarrow ^{\uparrow /\downarrow } c^{\prime } \in \mathcal {T}_{i}$ (e.g., $\texttt {dog} \rightarrow ^{\uparrow /\downarrow } \texttt {animal/terrier}$) first introduces a context for $c$ and then asks for an answer that identifies $c^{\prime }$ (which is also provided with a gloss so as to contain all available context). In the latter case, the rules $(\texttt {isa}^{r},c,c^{\prime }) \in \mathcal {T}_i$ in Table TABREF8 cover only direct ISA links from $c$ in direction $r \in \lbrace \uparrow ,\downarrow \rbrace $. In practice, for each $c$ and direction $r$, we construct tests that cover the set HOPS$(c,r)$ of all direct as well as derived ISA relations of $c$: This allows us to evaluate the extent to which models are able to handle complex forms of reasoning that require several inferential steps or hops. Dataset Probes and Construction ::: WordNetQA ::: Distractor Generation: @!START@$\textsc {distr}(\tau ^{\prime })$@!END@. An example of how distractors are generated is shown in Figure FIGREF6, which relies on similar principles as above. For each concept $c$, we choose 4 distractor answers that are close in the WordNet semantic space. For example, when constructing hypernymy tests for $c$ from the set hops$(c,\uparrow )$, we build distractors by drawing from $\textsc {hops}(c,\downarrow )$ (and vice versa), as well as from the $\ell $-deep sister family of $c$, defined as follows. The 1-deep sister family is simply $c$'s siblings or sisters, i.e., the other children $\tilde{c} \ne c$ of the parent node $c^{\prime }$ of $c$. For $\ell > 1$, the $\ell $-deep sister family also includes all descendants of each $\tilde{c}$ up to $\ell -1$ levels deep, denoted $\textsc {hops}_{\ell -1}(\tilde{c},\downarrow )$. Formally: For definitions and synonyms we build distractors from all of these sets (with a similar restriction on the depth of sister distractors as noted above). In doing this, we can systematically investigate model performance on a wide range of distractor sets. Dataset Probes and Construction ::: WordNetQA ::: Perturbations and Semantic Clusters Based on how we generate data, for each concept $c$ (i.e., atomic WordNet synset) and probe type (i.e., definitions, hypernymy, etc.), we have a wide variety of questions related to $c$ that manipulate 1) the complexity of reasoning that is involved (e.g., the number of inferential hops) and; 2) the types of distractors (or distractor perturbations) that are employed. We call such sets semantic clusters. As we describe in the next section, semantic clusters allow us to devise new types of evaluation that reveal whether models have comprehensive and consistent knowledge of target concepts (e.g., evaluating whether a model can correctly answer several questions associated with a concept, as opposed to a few disjoint instances). Details of the individual datasets are shown in Table TABREF12. From these sets, we follow BIBREF22 in allocating a maximum of 3k examples for training and reserve the rest for development and testing. Since we are interested in probing, having large held-out sets allows us to do detailed analysis and cluster-based evaluation. Dataset Probes and Construction ::: DictionaryQA The DictionaryQA dataset is created from the GCIDE dictionary, which is a comprehensive open-source English dictionary built largely from the Webster's Revised Unabridged Dictionary BIBREF38. Each entry consists of a word, its part-of-speech, its definition, and an optional example sentence (see Table TABREF14). Overall, 33k entries (out of a total of 155k) contain example sentences/usages. As with the WordNet probes, we focus on this subset so as to contextualize each word being probed. In contrast to WordNet, GCIDE does not have ISA relations or explicit synsets, so we take each unique entry to be a distinct sense. We then use the dictionary entries to create a probe that centers around word-sense disambiguation, as described below. Dataset Probes and Construction ::: DictionaryQA ::: Example and Distractor Generation. To generate gold questions and answers, we use the same generation templates for definitions exemplified in Figure TABREF8 for WordNetQA. To generate distractors, we simply take alternative definitions for the target words that represent a different word sense (e.g., the alternative definitions of gift shown in Table TABREF14), as well as randomly chosen definitions if needed to create a 5-way multiple choice question. As above, we reserve a maximum of 3k examples for training. Since we have only 9k examples in total in this dataset (see WordSense in Table TABREF12), we also reserve 3k each for development and testing. We note that initial attempts to build this dataset through standard random splitting gave rise to certain systematic biases that were exploited by the choice-only baseline models described in the next section, and hence inflated overall model scores. After several efforts at filtering we found that, among other factors, using definitions from entries without example sentences as distractors (e.g., the first two entries in Table TABREF14) had a surprising correlation with such biases. This suggests that possible biases involving differences between dictionary entries with and without examples can taint the resulting automatically generated MCQA dataset (for more discussion on the pitfalls involved with automatic dataset construction, see Section SECREF5). Probing Methodology and Modeling Given the probes above, we now can start to answer the empirical questions posed at the beginning. Our main focus is on looking at transformer-based MCQA models trained in the science domain (using the benchmarks shown in Table TABREF21). In this section, we provide details of MCQA and the target models, as well as several baselines that we use to sanity check our new datasets. To evaluate model competence, we look at a combination of model performance after science pre-training and after additional model fine-tuning using the lossless inoculation strategy of BIBREF22 (Section SECREF22). In Section SECREF24, we also discuss a cluster-level accuracy metric for measuring performance over semantic clusters. Probing Methodology and Modeling ::: Task Definition and Modeling Given a dataset $D =\lbrace (\textbf {q}^{(d)}, \lbrace a_{1}^{(d)},..., a_{N}^{(d)}\rbrace ) \rbrace _{d}^{\mid D \mid }$ consisting of pairs of questions stems $\textbf {q}$ and answer choices $a_{i}$, the goal is to find the correct answer $a_{i^{*}}$ that correctly answers each $\textbf {q}$. Throughout this paper, we look at 5-way multiple-choice problems (i.e., where each $N=5$). Probing Methodology and Modeling ::: Task Definition and Modeling ::: Question+Answer Encoder. To model this, our investigation centers around the use of the transformer-based BIBREF25 BERT encoder and fine-tuning approach of BIBREF7 (see also BIBREF6). For each question and individual answer pair $q^{(j)}_{a_{i}}$, we assume the following rendering of this input: which is run through the pre-trained BERT encoder to generate a representation for $ q^{(j)}_{a_{i}}$ using the hidden state representation for CLS (i.e., the classifier token) $\textbf {c}_{i}$: The probability of a given answer $p^{(j)}_{i}$ is then computed as $p^{(j)}_{i} \propto e^{\textbf {v}\cdot \textbf {c}^{(j)}_{i}}$, which uses an additional set of classification parameters $\textbf {v} \in \mathbb {R}^{H}$ that are optimized (along with the full transformer network) by taking the final loss of the probability of each correct answer $p_{i^{*}}$ over all answer choices: We specifically use BERT-large uncased with whole-word masking, as well as the RoBERTa-large model from BIBREF9, which is a more robustly trained version of the original BERT model. Our system uses the implementations provided in AllenNLP BIBREF39 and Huggingface BIBREF40. Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks. When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42). To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models. Following the notation from BIBREF0, for any given sequence $s$ of tokens in $\lbrace q^{(j)}, a_{1}^{(j)},...,a_{N}^{(j)}\rbrace $ in $D$, an encoding of $s$ is given as $h_{s}^{(j)} = \textbf {BiLSTM}(\textsc {embed}(s)) \in \mathbb {R}^{|s| \times 2h}$ (where $h$ is the dimension of the hidden state in each directional network, and embed$(\cdot )$ is an embedding function that assigns token-level embeddings to each token in $s$). A contextual representation for each $s$ is then built by applying an element-wise max operation over $h_{s}$ as follows: With these contextual representations, different baseline models can be constructed. For example, a Choice-Only model, which is a variant of the well-known hypothesis-only baseline used in NLI BIBREF46, scores each choice $c_{i}$ in the following way: for $\textbf {W}^{T} \in \mathbb {R}^{2h}$ independently of the question and assigns a probability to each answer $p_{i}^{(j)} \propto e^{\alpha _{i}^{(j)}}$. A slight variant of this model, the Choice-to-choice model, tries to single out a given answer choice relative to other choices by scoring all choice pairs $\alpha _{i,i^{\prime }}^{(j)} = \textsc {Att}(r^{(j)}_{c_{i}},r^{(j)}_{c_{i^{\prime }}}) \in \mathbb {R}$ using a learned attention mechanism Att and finding the choice with the minimal similarity to other options (for full details, see their original paper). In using these partial-input baselines, which we train directly on each target probe, we can check whether systematic biases related to answer choices were introduced into the data creation process. A Question-to-choice model, in contrast, uses the contextual representations for each question and individual choice and an attention model Att model to get a score $\alpha ^{(j)}_{q,i} = \textsc {Att}(r^{(j)}_{q},r^{(j)}_{c_{i}}) \in \mathbb {R}$ as above. Here we also experiment with using ESIM BIBREF47 to generate the contextual representations $r$, as well as a simpler VecSimilarity model that measures the average vector similarity between question and answer tokens: $\alpha ^{(j)}_{q,i} = \textsc {Sim}(\textsc {embed}(q^{(j)}),\textsc {embed}(c^{(j)}_{i}))$. In contrast to the models above, these sets of baselines are used to check for artifacts between questions and answers that are not captured in the partial-input baselines (see discussion in BIBREF49) and ensure that the overall MCQA tasks are sufficiently difficult for our transformer models. Probing Methodology and Modeling ::: Inoculation and Pre-training Using the various models introduced above, we train these models on benchmark tasks in the science domain and look at model performance on our probes with and without additional training on samples of probe data, building on the idea of inoculation from BIBREF23. Model inoculation is the idea of continuing to train models on new challenge tasks (in our cases, separately for each probe) using only a small amount of examples. Unlike in ordinary fine-tuning, the goal is not to learn an entirely re-purposed model, but to improve on (or vaccinate against) particular phenomena (e.g., our synthetic probes) that potentially deviate from a model's original training distribution (but that nonetheless might involve knowledge already contained in the model). In the variant proposed in BIBREF22, for each pre-trained (science) model and architecture $M_{a}$ we continue training the model on $k$ new probe examples (with a maximum of $k=$ 3k) under a set of different hyper-parameter configurations $j \in \lbrace 1, ..., J\rbrace $ and identify, for each $k$, the model $M_{*}^{a,k}$ with the best aggregate performance $S$ on the original (orig) and new task: As in BIBREF22, we found all models to be especially sensitive to different learning rates, and performed comprehensive hyper-parameters searches that also manipulate the number of iterations and random seeds used. Using this methodology, we can see how much exposure to new data it takes for a given model to master a new task, and whether there are phenomena that stress particular models (e.g., lead to catastrophic forgetting of the original task). Given the restrictions on the number of fine-tuning examples, our assumption is that when models are able to maintain good performance on their original task during inoculation, the quickness with which they are able to learn the inoculated task provides evidence of prior competence, which is precisely what we aim to probe. To measure past performance, we define a model's inoculation cost as the difference in the performance of this model on its original task before and after inoculation. We pre-train on an aggregated training set of the benchmark science exams detailed in Table TABREF21, and created an aggregate development set of around 4k science questions for evaluating overall science performance and inoculation costs. To handle the mismatch between number of answer choices in these sets, we made all sets 5-way by adding empty answers as needed. We also experimented with a slight variant of inoculation, called add-some inoculation, which involves balancing the inoculation training sets with naturalistic science questions. We reserve the MCQL dataset in Table TABREF21 for this purpose, and experiment with balancing each probe example with a science example (x1 matching) and adding twice as many science questions (x2 matching, up to 3k) for each new example. Probing Methodology and Modeling ::: Evaluating Model Competence The standard way to evaluate our MCQA models is by looking at the overall accuracy of the correct answer prediction, or what we call instance-level accuracy (as in Table TABREF25). Given the nature of our data and the existence of semantic clusters as detailed in Section SECREF11 (i.e., sets of questions and answers under different distractor choices and inference complexity), we also measure a model's cluster-level (or strict cluster) accuracy, which requires correctly answering all questions in a cluster. Example semantic clusters are shown in Table TABREF30; in the first case, there are 6 ISA$^\uparrow $ questions (including perturbations) about the concept trouser.n.01 (e.g., involving knowing that trousers are a type of consumer good and garment/clothing), which a model must answer in order to receive full credit. Our cluster-based analysis is motivated by the idea that if a model truly knows the meaning of a given concept, such as the concept of trousers, then it should be able to answer arbitrary questions about this concept without sensitivity to varied distractors. While our strict cluster metric is simplistic, it takes inspiration from work on visual QA BIBREF53, and allows us to evaluate how consistent and robust models are across our different probes, and to get insight into whether errors are concentrated on a small set of concepts or widespread across clusters. Results and Findings In this section, we provide the results of the empirical questions first introduced in Figure FIGREF1, starting with the results of our baseline models. Results and Findings ::: Are our Probes Sufficiently Challenging? As shown in Table TABREF25, most of our partial-input baselines (i.e., Choice-Only and Choice-to-Choice models) failed to perform well on our dataset probes across a wide range of models, showing that such probes are generally immune from biases relating to how distractors were generated. As already discussed in Section SECREF13, however, initial versions of the DictionaryQA dataset had unforeseen biases partly related to whether distractors were sampled from entries without example sentences, which resulted in high Choice-Only-GloVe scores ranging around 56% accuracy before a filtering step was applied to remove these distractors. We had similar issues with the hypernymy probe which, even after a filtering step that used our Choice-to-Choice-GloVe model, still leads to high results on the BERT and RoBERTa choice-only models. Given that several attempts were made to entirely de-duplicate the different splits (both in terms of gold answers and distractor types), the source of these biases is not at all obvious, which shows how easy it is for unintended biases in expert knowledge to appear in the resulting datasets and the importance of having rigorous baselines. We also note the large gap in some cases between the BERT and RoBERTa versus GloVe choice-only models, which highlights the need for having partial-input baselines that use the best available models. Using a more conventional set of Task-Specific QA models (i.e., the LSTM-based Question-to-Choice models trained directly on the probes), we can see that results are not particularly strong on any of the datasets, suggesting that our probes are indeed sufficiently challenging and largely immune from overt artifacts. The poor performance of the VecSimilarity (which uses pre-trained Word2Vec embeddings without additional training) provides additional evidence that elementary lexical matching strategies are insufficient for solving any of the probing tasks. Results and Findings ::: How well do pre-trained MCQA models do? Science models that use non-transformer based encoders, such as the ESIM model with GloVe and ELMO, perform poorly across all probes, in many cases scoring near random chance, showing limits to how well they generalize from science to other tasks even with pre-trained GloVe and ELMO embeddings. In sharp contrast, the transformer models have mixed results, the most striking result being the RoBERTa models on the definitions and synonymy probes (achieving a test accuracy of 77% and 61%, respectively), which outperform several of the task-specific LSTM models trained directly on the probes. At first glance, this suggests that RoBERTa, which generally far outpaces even BERT across most probes, has high competence of definitions and synonyms even without explicit training on our new tasks. Given the controlled nature of our probes, we can get a more detailed view of how well the science models are performing across different reasoning and distractor types, as shown in the first column of Figure FIGREF28 for ESIM and RoBERTa. The ESIM science model without training has uniformly poor performance across all categories, whereas the performance of RoBERTa is more varied. Across all datasets and number of hops (i.e., the rows in the heat maps), model performance for RoBERTa is consistently highest among examples with random distractors (i.e., the first column), and lowest in cases involving distractors that are closest in WordNet space (e.g., sister and ISA, or up/down, distractors of distance $k^{\prime }=1$). This is not surprising, given that, in the first case, random distractors are likely to be the easiest category (and the opposite for distractors close in space), but suggests that RoBERTa might only be getting the easiest cases correct. Model performance also clearly degrades for hypernymy and hyponymy across all models as the number of hops $k$ increases (see red dashed boxes). For example, problems that involve hyponym reasoning with sister distractors of distance $k^{\prime }=1$ (i.e., the second column) degrades from 47% to 15% when the number of hops $k$ increases from 1 to 4. This general tendency persists even after additional fine-tuning, as we discuss next, and gives evidence that models are limited in their capacity for certain types of multi-hop inferences. As discussed by BIBREF26, the choice of generation templates can have a significant effect on model performance. The results so far should therefore be regarded as a lower bound on model competence. It is possible that model performance is high for definitions, for example, because the associated templates best align with the science training distribution (which we know little about). For this reason, the subsequent inoculation step is important—it gives the model an opportunity to learn about our target templates and couple this learned knowledge with its general knowledge acquired during pre-training and science training (which is, again, what we aim to probe). Results and Findings ::: Can Models Be Effectively Inoculated? Model performance after additional fine-tuning, or inoculation, is shown in the last 3 rows of Table TABREF25, along with learning curves shown in Figure FIGREF29 for a selection of probes and models. In the former case, the performance represents the model (and inoculation amount) with the highest aggregate performance over the old task and new probe. Here we again see the transformer-based models outperform non-transformer models, and that better models correlate with lower inoculation costs. For example, when inoculating on synonymy, the cost for ESIM is around 7% reduced accuracy on its original task, as opposed to $< 1$% and around 1% for BERT and RoBERTa, respectively. This shows the high capacity for transformer models to absorb new tasks with minimal costs, as also observed in BIBREF22 for NLI. As shown in Figure FIGREF29, transformer models tend to learn most tasks fairly quickly while keeping constant scores on their original tasks (i.e., the flat dashed lines observed in plots 1-4), which gives evidence of high competence. In both cases, add-some inoculation proves to be a cheap and easy way to 1) improve scores on the probing tasks (i.e., the solid black and blue lines in plot 1) and; 2) minimize loss on science (e.g., the blue and black dashed lines in plots 2-4). The opposite is the case for ESIM (plots 5-6); models are generally unable to simultaneously learn individual probes without degrading on their original task, and adding more science data during inoculation confuses models on both tasks. As shown in Figure FIGREF28, RoBERTa is able to significantly improve performance across most categories even after inoculation with a mere 100 examples (the middle plot), which again provides strong evidence of prior competence. As an example, RoBERTa improves on 2-hop hyponymy inference with random distractors by 18% (from 59% to 77%). After 3k examples, the model has high performance on virtually all categories (the same score increases from 59% to 87%), however results still tends to degrade as a function of hop and distractor complexity, as discussed above. Despite the high performance of our transformer models after inoculation, model performance on most probes (with the exception of Definitions) averages around 80% for our best models. This suggests that there is still considerable room for improvement, especially for synonymy and word sense, which is a topic that we discuss more in Section SECREF6. Results and Findings ::: Are Models Consistent across Clusters? Table TABREF32 shows cluster-level accuracies for the different WordNetQA probes. As with performance across the different inference/distractor categories, these results are mixed. For some probes, such as definitions, our best models appear to be rather robust; e.g., our RoBERTa model has a cluster accuracy of $75\%$, meaning that it can answer all questions perfectly for 75% of the target concepts and that errors are concentrated on a small minority (25%) of concepts. On synonymy and hypernymy, both BERT and RoBERTa appear robust on the majority of concepts, showing that errors are similarly concentrated. In contrast, our best model on hyponymy has an accuracy of 36%, meaning that its errors are spread across many concepts, thus suggesting less robustness. Table TABREF30 shows a selection of semantic clusters involving ISA reasoning, as well as the model performance over different answers (shown symbolically) and perturbations. For example, in the the second case, the cluster is based around the concept/synset oppose.v.06 and involves 4 inferences and a total 24 questions (i.e., inferences with perturbations). Our weakest model, ESIM, answers only 5 out of 24 questions correctly, whereas RoBERTa gets 21/24. In the other cases, RoBERTa gets all clusters correct, whereas BERT and ESIM get none of them correct. We emphasize that these results only provide a crude look into model consistency and robustness. Recalling again the details in Table TABREF12, probes differ in terms of average size of clusters. Hyponymy, in virtue of having many more questions per cluster, might simply be a much more difficult dataset. In addition, such a strict evaluation does not take into account potential errors inside of clusters, which is an important issue that we discuss in the next section. We leave addressing such issues and coming up with more insightful cluster-based metrics for future work. Discussion and Conclusion We presented several new challenge datasets and a novel methodology for automatically building such datasets from knowledge graphs and taxonomies. We used these to probe state-of-the-art open-domain QA models (centering around models based on variants of BERT). While our general methodology is amendable to any target knowledge resource or QA model/domain, we focus on probing definitions and ISA knowledge using open-source dictionaries and MCQA models trained in the science domain. We find, consistent with recent probing studies BIBREF26, that transformer-based models have a remarkable ability to answer questions that involve complex forms of relational knowledge, both with and without explicit exposure to our new target tasks. In the latter case, a newer RoBERTa model trained only on benchmark science tasks is able to outperform several task-specific LSTM-based models trained directly on our probing data. When re-trained on small samples (e.g., 100 examples) of probing data using variations of the lossless inoculation strategy from BIBREF22, RoBERTa is able to master many aspects of our probes with virtually no performance loss on its original QA task. These positive results suggest that transformer-based models, especially models additionally fine-tuned on small samples of synthetic data, can be used in place of task-specific models used for querying relational knowledge, as has already been done for targeted tasks such as word sense disambiguation BIBREF54. Since models seem to already contain considerable amounts of relational knowledge, our simple inoculation strategy, which tries to nudge models to bring out this knowledge explicitly, could serve as a cheaper alternative to recent attempts to build architectures that explicitly incorporate structured knowledge BIBREF55; we see many areas where our inoculation strategy could be improved for such purposes, including having more complex loss functions that manage old and new information, as well as using techniques that take into account network plasticity BIBREF56. The main appeal of using automatically generate datasets is the ability to systematically manipulate and control the complexity of target questions, which allows for more controlled experimentation and new forms of evaluation. Despite the positive results described above, results that look directly at the effect of different types of distractors and the complexity of reasoning show that our best models, even after additional fine-tuning, struggle with certain categories of hard distractors and multi-hop inferences. For some probes, our cluster-based analysis also reveals that errors are widespread across concept clusters, suggesting that models are not always consistent and robust. These results, taken together with our findings about the vulnerability of synthetic datasets to systematic biases, suggest that there is much room for improvement and that the positive results should be taken with a grain of salt. Developing better ways to evaluate semantic clusters and model robustness would be a step in this direction. We emphasize that using synthetic versus naturalistic QA data comes with important trade-offs. While we are able to generate large amounts of systematically controlled data at virtually no cost or need for manual annotation, it is much harder to validate the quality of such data at such a scale and such varying levels of complexity. Conversely, with benchmark QA datasets, it is much harder to perform the type of careful manipulations and cluster-based analyses we report here. While we assume that the expert knowledge we employ, in virtue of being hand-curated by human experts, is generally correct, we know that such resources are fallible and error-prone. Initial crowd-sourcing experiments that look at validating samples of our data show high agreement across probes and that human scores correlate with the model trends across the probe categories. More details of these studies are left for future work.
How do they control for annotation artificats?
85590bb26fed01a802241bc537d85ba5ef1c6dc2
How Ġdo Ġthey Ġcontrol Ġfor Ġannotation Ġartific ats ?
Ġwe Ġuse Ġseveral Ġof Ġthe ĠMC Q A Ġbaseline Ġmodels Ġfirst Ġintroduced Ġin ĠB IB REF 0
<s> How Ġdo Ġthey Ġcontrol Ġfor Ġannotation Ġartific ats ? </s> Introduction </s> Autom atically Ġanswering Ġquestions , Ġespecially Ġin Ġthe Ġopen - domain Ġsetting Ġ( i . e ., Ġwhere Ġminimal Ġor Ġno Ġcontextual Ġknowledge Ġis Ġexplicitly Ġprovided ), Ġrequires Ġbringing Ġto Ġbear Ġconsiderable Ġamount Ġof Ġbackground Ġknowledge Ġand Ġreasoning Ġabilities . ĠFor Ġexample , Ġknowing Ġthe Ġanswers Ġto Ġthe Ġtwo Ġquestions Ġin ĠFigure ĠFIG REF 1 Ġrequires Ġidentifying Ġa Ġspecific ĠIS A Ġrelation Ġ( i . e ., Ġthat Ġcooking Ġis Ġa Ġtype Ġof Ġlearned Ġbehavior ) Ġas Ġwell Ġas Ġrecalling Ġthe Ġdefinition Ġof Ġa Ġconcept Ġ( i . e ., Ġthat Ġglobal Ġwarming Ġis Ġdefined Ġas Ġa Ġworldwide Ġincrease Ġin Ġtemperature ). ĠIn Ġthe Ġmultiple - choice Ġsetting , Ġwhich Ġis Ġthe Ġvariety Ġof Ġquestion - ans w ering Ġ( Q A ) Ġthat Ġwe Ġfocus Ġon Ġin Ġthis Ġpaper , Ġthere Ġis Ġalso Ġpragmatic Ġreasoning Ġinvolved Ġin Ġselecting Ġoptimal Ġanswer Ġchoices Ġ( e . g ., Ġwhile Ġgreenhouse Ġeffect Ġmight Ġin Ġsome Ġother Ġcontext Ġbe Ġa Ġreasonable Ġanswer Ġto Ġthe Ġsecond Ġquestion Ġin ĠFigure ĠFIG REF 1 , Ġglobal Ġwarming Ġis Ġa Ġpreferable Ġcandidate ). </s> Recent Ġsuccesses Ġin ĠQ A , Ġdriven Ġlargely Ġby Ġthe Ġcreation Ġof Ġnew Ġresources ĠB IB REF 2 , ĠB IB REF 3 , ĠB IB REF 4 , ĠB IB REF 5 Ġand Ġadvances Ġin Ġmodel Ġpre - training ĠB IB REF 6 , ĠB IB REF 7 , Ġraise Ġa Ġnatural Ġquestion : Ġdo Ġstate - of - the - art Ġmultiple - choice ĠQ A Ġ( MC Q A ) Ġmodels Ġthat Ġexcel Ġat Ġstandard Ġtasks Ġreally Ġhave Ġbasic Ġknowledge Ġand Ġreasoning Ġskills ? </s> Most Ġexisting ĠMC Q A Ġdatasets Ġare Ġconstructed Ġthrough Ġeither Ġexpensive Ġcrowd - s ourcing ĠB IB REF 8 Ġor Ġhand Ġengineering Ġeffort , Ġin Ġthe Ġformer Ġcase Ġmaking Ġit Ġpossible Ġto Ġcollect Ġlarge Ġamounts Ġof Ġdata Ġat Ġthe Ġcost Ġof Ġlosing Ġsystematic Ġcontrol Ġover Ġthe Ġsemantics Ġof Ġthe Ġtarget Ġquestions . ĠHence , Ġdoing Ġa Ġcontrolled Ġexperiment Ġto Ġanswer Ġsuch Ġa Ġquestion Ġfor ĠQ A Ġis Ġdifficult Ġgiven Ġa Ġlack Ġof Ġtargeted Ġchallenge Ġdatasets . </s> Having Ġdefinitive Ġempirical Ġevidence Ġof Ġmodel Ġcompetence Ġon Ġany Ġgiven Ġphenomenon Ġrequires Ġconstructing Ġa Ġwide Ġrange Ġof Ġsystematic Ġtests . ĠFor Ġexample , Ġin Ġmeasuring Ġcompetence Ġof Ġdefinitions , Ġnot Ġonly Ġdo Ġwe Ġwant Ġto Ġsee Ġthat Ġthe Ġmodel Ġcan Ġhandle Ġindividual Ġquestions Ġsuch Ġas ĠFigure ĠFIG REF 1 . 1 Ġinside Ġof Ġbenchmark Ġtasks , Ġbut Ġthat Ġit Ġcan Ġanswer Ġa Ġwider Ġrange Ġof Ġquestions Ġthat Ġexhaust ively Ġcover Ġa Ġbroad Ġset Ġof Ġconcepts Ġand Ġquestion Ġpert urb ations Ġ( i . e ., Ġsystematic Ġadjustments Ġto Ġhow Ġthe Ġquestions Ġare Ġconstructed ). ĠThe Ġsame Ġapplies Ġto ĠIS A Ġreasoning ; Ġnot Ġonly Ġis Ġit Ġimportant Ġto Ġrecognize Ġin Ġthe Ġquestion Ġin ĠFigure ĠFIG REF 1 . 1 Ġthat Ġcooking Ġis Ġa Ġlearned Ġbehavior , Ġbut Ġalso Ġthat Ġcooking Ġis Ġa Ġgeneral Ġtype Ġof Ġbehavior Ġor , Ġthrough Ġa Ġfew Ġmore Ġinf erential Ġsteps , Ġa Ġtype Ġof Ġhuman Ġactivity . </s> In Ġthis Ġpaper , Ġwe Ġlook Ġat Ġsystematically Ġconstructing Ġsuch Ġtests Ġby Ġexploiting Ġthe Ġvast Ġamounts Ġof Ġstructured Ġinformation Ġcontained Ġin Ġvarious Ġtypes Ġof Ġexpert Ġknowledge Ġsuch Ġas Ġknowledge Ġgraphs Ġand Ġlex ical Ġtax onom ies . ĠOur Ġgeneral Ġmethodology Ġworks Ġas Ġillustrated Ġin ĠFigure ĠFIG REF 1 : Ġgiven Ġany ĠMC Q A Ġmodel Ġtrained Ġon Ġa Ġset Ġof Ġbenchmark Ġtasks , Ġwe Ġsystematically Ġgenerate Ġa Ġset Ġof Ġsynthetic Ġdataset Ġprobes Ġ( i . e ., ĠMC Q A Ġrender ings Ġof Ġthe Ġtarget Ġinformation ) Ġfrom Ġinformation Ġin Ġexpert Ġknowledge Ġsources . ĠWe Ġthen Ġuse Ġthese Ġprobes Ġto Ġask Ġtwo Ġempirical Ġquestions : Ġ1 ) Ġhow Ġwell Ġdo Ġmodels Ġtrained Ġon Ġbenchmark Ġtasks Ġperform Ġon Ġthese Ġprobing Ġtasks Ġand ; Ġ2 ) Ġcan Ġsuch Ġmodels Ġbe Ġre - trained Ġto Ġmaster Ġnew Ġchallenges Ġwith Ġminimal Ġperformance Ġloss Ġon Ġtheir Ġoriginal Ġtasks ? </s> While Ġour Ġmethodology Ġis Ġam enable Ġto Ġany Ġknowledge Ġsource Ġand Ġset Ġof Ġmodels / bench mark Ġtasks , Ġwe Ġfocus Ġon Ġprobing Ġstate - of - the - art Ġtransformer Ġmodels ĠB IB REF 7 , ĠB IB REF 9 Ġin Ġthe Ġdomain Ġof Ġscience ĠMC Q A . ĠFor Ġsources Ġof Ġexpert Ġknowledge , Ġwe Ġuse ĠWord Net , Ġa Ġcomprehensive Ġlex ical Ġont ology , Ġand Ġother Ġpublicly Ġavailable Ġdictionary Ġresources . ĠWe Ġdevise Ġprobes Ġthat Ġmeasure Ġmodel Ġcompetence Ġin Ġdefinition Ġand Ġtax onomic Ġknowledge Ġin Ġdifferent Ġsettings Ġ( including Ġhyper ny my , Ġhyp ony my , Ġand Ġsyn ony my Ġdetection , Ġand Ġword Ġsense Ġdis amb ig uation ). ĠThis Ġchoice Ġis Ġmotivated Ġby Ġfact Ġthat Ġthe Ġscience Ġdomain Ġis Ġconsidered Ġparticularly Ġchallenging Ġfor ĠQ A ĠB IB REF 10 , ĠB IB REF 11 , ĠB IB REF 12 , Ġand Ġexisting Ġscience Ġbenchmarks Ġare Ġknown Ġto Ġinvolve Ġwidespread Ġuse Ġof Ġsuch Ġknowledge Ġ( see ĠB IB REF 1 , ĠB IB REF 13 Ġfor Ġanalysis ), Ġwhich Ġis Ġalso Ġarguably Ġfundamental Ġto Ġmore Ġcomplex Ġforms Ġof Ġreasoning . </s> We Ġshow Ġthat Ġaccurately Ġprobing ĠQ A Ġmodels Ġvia Ġsynthetic Ġdatasets Ġis Ġnot Ġstraightforward , Ġas Ġunexpected Ġartifacts Ġcan Ġeasily Ġarise Ġin Ġsuch Ġdata . ĠThis Ġmotiv ates Ġour Ġcarefully Ġconstructed Ġbas elines Ġand Ġclose Ġdata Ġinspection Ġto Ġensure Ġprobe Ġquality . </s> Our Ġresults Ġconfirm Ġthat Ġtransformer - based ĠQ A Ġmodels Ġhave Ġa Ġremarkable Ġability Ġto Ġrecognize Ġcertain Ġtypes Ġof Ġknowledge Ġcaptured Ġin Ġour Ġprobes âĢĶ even Ġwithout Ġadditional Ġfine - tun ing . ĠSuch Ġmodels Ġcan Ġeven Ġoutper form Ġstrong Ġtask - specific Ġmodels Ġtrained Ġdirectly Ġon Ġour Ġprobing Ġtasks Ġ( e . g ., Ġon Ġdefinitions , Ġour Ġbest Ġmodel Ġachieves Ġ77 % Ġtest Ġaccuracy Ġwithout Ġspecialized Ġtraining , Ġas Ġopposed Ġto Ġ51 % Ġfor Ġa Ġtask - specific ĠL ST M - based Ġmodel ). ĠWe Ġalso Ġshow Ġthat Ġthe Ġsame Ġmodels Ġcan Ġbe Ġeffectively Ġre - fine - tun ed Ġon Ġsmall Ġsamples Ġ( even Ġ100 Ġexamples ) Ġof Ġprobe Ġdata , Ġand Ġthat Ġhigh Ġperformance Ġon Ġthe Ġprobes Ġtends Ġto Ġcorrelate Ġwith Ġa Ġsmaller Ġdrop Ġin Ġthe Ġmodel 's Ġperformance Ġon Ġthe Ġoriginal ĠQ A Ġtask . </s> Our Ġcomprehensive Ġassessment Ġreveals Ġseveral Ġinteresting Ġnuances Ġto Ġthe Ġoverall Ġpositive Ġtrend . ĠFor Ġexample , Ġthe Ġperformance Ġof Ġeven Ġthe Ġbest ĠQ A Ġmodels Ġde grades Ġsubstantially Ġon Ġour Ġhyp onym Ġprobes Ġ( by Ġ8 - 15 %) Ġwhen Ġgoing Ġfrom Ġ1 - hop Ġlinks Ġto Ġ2 - hops . ĠFurther , Ġthe Ġaccuracy Ġof Ġeven Ġour Ġbest Ġmodels Ġon Ġthe ĠWord Net Q A Ġprobe Ġdrops Ġby Ġ14 - 44 % Ġunder Ġour Ġcluster - based Ġanalysis , Ġwhich Ġassess es Ġwhether Ġa Ġmodel Ġknows Ġseveral Ġfacts Ġabout Ġeach Ġindividual Ġconcept , Ġrather Ġthan Ġjust Ġbeing Ġgood Ġat Ġanswering Ġisolated Ġquestions . ĠState - of - the - art ĠQ A Ġmodels Ġthus Ġhave Ġmuch Ġroom Ġto Ġimprove Ġeven Ġin Ġsome Ġfundamental Ġbuilding Ġblocks , Ġnamely Ġdefinitions Ġand Ġtax onomic Ġhierarch ies , Ġof Ġmore Ġcomplex Ġforms Ġof Ġreasoning . </s> Related ĠWork </s> We Ġfollow Ġrecent Ġwork Ġon Ġconstructing Ġchallenge Ġdatasets Ġfor Ġprobing Ġneural Ġmodels , Ġwhich Ġhas Ġprimarily Ġfocused Ġon Ġthe Ġtask Ġof Ġnatural Ġlanguage Ġinference Ġ( N LI ) ĠB IB REF 14 , ĠB IB REF 15 , ĠB IB REF 16 , ĠB IB REF 17 , ĠB IB REF 18 . ĠMost Ġof Ġthis Ġwork Ġlooks Ġat Ġconstructing Ġdata Ġthrough Ġadvers arial Ġgeneration Ġmethods , Ġwhich Ġhave Ġalso Ġbeen Ġfound Ġuseful Ġfor Ġcreating Ġstronger Ġmodels ĠB IB REF 19 . ĠThere Ġhas Ġalso Ġbeen Ġwork Ġon Ġusing Ġsynthetic Ġdata Ġof Ġthe Ġtype Ġwe Ġconsider Ġin Ġthis Ġpaper ĠB IB REF 20 , ĠB IB REF 21 , ĠB IB REF 22 . ĠWe Ġclosely Ġfollow Ġthe Ġmethodology Ġof ĠB IB REF 22 , Ġwho Ġuse Ġhand - con structed Ġlinguistic Ġfragments Ġto Ġprobe ĠNL I Ġmodels Ġand Ġstudy Ġmodel Ġre - training Ġusing Ġa Ġvariant Ġof Ġthe Ġinoc ulation Ġby Ġfine - tun ing Ġstrategy Ġof ĠB IB REF 23 . ĠIn Ġcontrast , Ġwe Ġfocus Ġon Ġprobing Ġopen - domain ĠMC Q A Ġmodels Ġ( see ĠB IB REF 24 Ġfor Ġa Ġrelated Ġstudy Ġin Ġthe Ġreading Ġcomprehension Ġsetting ) Ġas Ġwell Ġas Ġconstructing Ġdata Ġfrom Ġmuch Ġlarger Ġsources Ġof Ġstructured Ġknowledge . </s> Our Ġmain Ġstudy Ġfocuses Ġon Ġprobing Ġthe ĠB ERT Ġmodel Ġand Ġfine - tun ing Ġapproach Ġof ĠB IB REF 7 , Ġand Ġother Ġvariants Ġthereof , Ġwhich Ġare Ġall Ġbased Ġon Ġthe Ġtransformer Ġarchitecture Ġof ĠB IB REF 25 . ĠRelated Ġto Ġour Ġefforts , Ġthere Ġhave Ġbeen Ġrecent Ġstudies Ġinto Ġthe Ġtypes Ġof Ġrelational Ġknowledge Ġcontained Ġin Ġlarge - scale Ġknowledge Ġmodels ĠB IB REF 26 , ĠB IB REF 27 , ĠB IB REF 28 , Ġwhich , Ġsimilar Ġto Ġour Ġwork , Ġprobe Ġmodels Ġusing Ġstructured Ġknowledge Ġsources . ĠThis Ġprior Ġwork , Ġhowever , Ġprimarily Ġfocuses Ġon Ġun ear thing Ġthe Ġknowledge Ġcontained Ġin Ġthe Ġunderlying Ġlanguage Ġmodels Ġas Ġis Ġwithout Ġfurther Ġtraining , Ġusing Ġsimple Ġ( single Ġtoken ) Ġclo ze - style Ġprobing Ġtasks Ġand Ġtemplates Ġ( similar Ġto Ġwhat Ġwe Ġpropose Ġin ĠSection ĠSEC REF 3 ). ĠIn Ġcontrast , Ġwe Ġfocus Ġon Ġunderstanding Ġthe Ġknowledge Ġcontained Ġin Ġlanguage Ġmodels Ġafter Ġthey Ġhave Ġbeen Ġtrained Ġfor Ġa ĠQ A Ġend - task Ġusing Ġbenchmark Ġdatasets Ġin Ġwhich Ġsuch Ġknowledge Ġis Ġexpected Ġto Ġbe Ġwidespread . ĠFurther , Ġour Ġevaluation Ġis Ġdone Ġbefore Ġand Ġafter Ġthese Ġmodels Ġare Ġfine - tun ed Ġon Ġour Ġprobe ĠQ A Ġtasks , Ġusing Ġa Ġmore Ġcomplex Ġset Ġof ĠQ A Ġtemplates Ġand Ġtarget Ġinf erences . </s> The Ġuse Ġof Ġlex ical Ġresources Ġand Ġknowledge Ġgraphs Ġsuch Ġas ĠWord Net Ġto Ġconstruct Ġdatasets Ġhas Ġa Ġlong Ġhistory , Ġand Ġhas Ġrecently Ġappeared Ġin Ġwork Ġon Ġadvers arial Ġattacks ĠB IB REF 14 , ĠB IB REF 29 Ġand Ġgeneral Ġtask Ġconstruction ĠB IB REF 30 , ĠB IB REF 31 . ĠIn Ġthe Ġarea Ġof ĠMC Q A , Ġthere Ġis Ġrelated Ġwork Ġon Ġconstructing Ġquestions Ġfrom Ġtu ples ĠB IB REF 32 , ĠB IB REF 3 , Ġboth Ġof Ġwhich Ġinvolve Ġstandard Ġcrowd Ġannotation Ġto Ġelicit Ġquestion - answer Ġpairs Ġ( see Ġalso ĠB IB REF 33 , ĠB IB REF 34 ). ĠIn Ġcontrast Ġto Ġthis Ġwork , Ġwe Ġfocus Ġon Ġgenerating Ġdata Ġin Ġan Ġentirely Ġautomatic Ġfashion , Ġwhich Ġob vi ates Ġthe Ġneed Ġfor Ġexpensive Ġannotation Ġand Ġgives Ġus Ġthe Ġflexibility Ġto Ġconstruct Ġmuch Ġlarger Ġdatasets Ġthat Ġcontrol Ġa Ġrich Ġset Ġof Ġsemantic Ġaspects Ġof Ġthe Ġtarget Ġquestions . </s> Dat as et ĠPro bes Ġand ĠConstruction </s> Our Ġprobing Ġmethodology Ġstarts Ġby Ġconstructing Ġchallenge Ġdatasets Ġ( Figure ĠFIG REF 1 , Ġyellow Ġbox ) Ġfrom Ġa Ġtarget Ġset Ġof Ġknowledge Ġresources . ĠEach Ġof Ġour Ġprobing Ġdatasets Ġconsists Ġof Ġmultiple - choice Ġquestions Ġthat Ġinclude Ġa Ġquestion Ġ$\ text bf Ġ{ q } $ Ġand Ġa Ġset Ġof Ġanswer Ġchoices Ġor Ġcandidates Ġ$\ l brace Ġa _{ 1 }, ... a _{ N }\ r brace Ġ$ . ĠThis Ġsection Ġdescribes Ġin Ġdetail Ġthe Ġ5 Ġdifferent Ġdatasets Ġwe Ġbuild , Ġwhich Ġare Ġdrawn Ġfrom Ġtwo Ġsources Ġof Ġexpert Ġknowledge , Ġnamely ĠWord Net ĠB IB REF 35 Ġand Ġthe ĠGNU ĠCollabor ative ĠInternational ĠDictionary Ġof ĠEnglish Ġ( GC IDE ). ĠWe Ġdescribe Ġeach Ġresource Ġin Ġturn , Ġand Ġexplain Ġhow Ġthe Ġresulting Ġdataset Ġprobes , Ġwhich Ġwe Ġcall ĠWord Net Q A Ġand ĠDictionary Q A , Ġare Ġconstructed . </s> For Ġconvenience , Ġwe Ġwill Ġdescribe Ġeach Ġsource Ġof Ġexpert Ġknowledge Ġas Ġa Ġdirected , Ġedge - label ed Ġgraph Ġ$ G $. ĠThe Ġnodes Ġof Ġthis Ġgraph Ġare Ġ$\ math cal Ġ{ V } Ġ= Ġ\ math cal Ġ{ C } Ġ\ cup Ġ\ math cal Ġ{ W } Ġ\ cup Ġ\ math cal Ġ{ S } Ġ\ cup Ġ\ math cal Ġ{ D } $, Ġwhere Ġ$\ math cal Ġ{ C } $ Ġis Ġa Ġset Ġof Ġatomic Ġconcepts , Ġ$\ math cal Ġ{ W } $ Ġa Ġset Ġof Ġwords , Ġ$\ math cal Ġ{ S } $ Ġa Ġset Ġof Ġsentences , Ġand Ġ$\ math cal Ġ{ D } $ Ġa Ġset Ġof Ġdefinitions Ġ( see ĠTable ĠT AB REF 4 Ġfor Ġdetails Ġfor ĠWord Net Ġand ĠGC IDE ). ĠEach Ġedge Ġof Ġ$ G $ Ġis Ġdirected Ġfrom Ġan Ġatomic Ġconcept Ġin Ġ$\ math cal Ġ{ C } $ Ġto Ġanother Ġnode Ġin Ġ$ V $, Ġand Ġis Ġlabeled Ġwith Ġa Ġrelation , Ġsuch Ġas Ġhyper ny m Ġor Ġis a $ ^ \ up arrow Ġ$ , Ġfrom Ġa Ġset Ġof Ġrelations Ġ$\ math cal Ġ{ R } $ Ġ( see ĠTable ĠT AB REF 4 ). </s> When Ġdefining Ġour Ġprobe Ġquestion Ġtemplates , Ġit Ġwill Ġbe Ġuseful Ġto Ġview Ġ$ G $ Ġas Ġa Ġset Ġof Ġ( relation , Ġsource , Ġtarget ) Ġtri ples Ġ$\ math cal Ġ{ T } Ġ\ sub set eq Ġ\ math cal Ġ{ R } Ġ\ times Ġ\ math cal Ġ{ C } Ġ\ times Ġ\ math cal Ġ{ V } $. ĠDue Ġto Ġtheir Ġorigin Ġin Ġan Ġexpert Ġknowledge Ġsource , Ġsuch Ġtri ples Ġpreserve Ġsemantic Ġconsistency . ĠFor Ġinstance , Ġwhen Ġthe Ġrelation Ġin Ġa Ġtriple Ġis Ġdef , Ġthe Ġcorresponding Ġedge Ġmaps Ġa Ġconcept Ġin Ġ$\ math cal Ġ{ C } $ Ġto Ġa Ġdefinition Ġin Ġ$\ math cal Ġ{ D } $. </s> To Ġconstruct Ġprobe Ġdatasets , Ġwe Ġrely Ġon Ġtwo Ġhe uristic Ġfunctions , Ġdefined Ġbelow Ġfor Ġeach Ġindividual Ġprobe : Ġ$\ text sc Ġ{ gen } _{ \ math cal Ġ{ Q }} (\ t au Ġ) $, Ġwhich Ġgenerates Ġgold Ġquestion - answer Ġpairs Ġ$( \ text bf Ġ{ q }, \ text bf Ġ{ a }) $ Ġfrom Ġa Ġset Ġof Ġtri ples Ġ$\ t au Ġ\ sub set eq Ġ\ math cal Ġ{ T } $ Ġand Ġquestion Ġtemplates Ġ$\ math cal Ġ{ Q } $, Ġand Ġ$\ text sc Ġ{ dist r } (\ t au Ġ^ {\ prime Ġ}) $, Ġwhich Ġgenerates Ġdistract or Ġanswers Ġchoices Ġ$\ l brace Ġa ^ {\ prime Ġ} _{ 1 }, ... a ^ {\ prime Ġ} _{ N - 1 } Ġ\ r brace Ġ$ Ġbased Ġon Ġanother Ġset Ġof Ġtri ples Ġ$\ t au Ġ^ {\ prime Ġ} $ Ġ( where Ġusually Ġ$\ t au Ġ\ sub set Ġ\ t au Ġ^ {\ prime Ġ} $ ). ĠFor Ġbre vity , Ġwe Ġwill Ġuse Ġ$\ text sc Ġ{ gen } (\ t au Ġ) $ Ġto Ġdenote Ġ$\ text sc Ġ{ gen } _{ \ math cal Ġ{ Q }} (\ t au Ġ) $, Ġleaving Ġquestion Ġtemplates Ġ$\ math cal Ġ{ Q } $ Ġimplicit . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠWord Net Q A </s> Word Net Ġis Ġan ĠEnglish Ġlex ical Ġdatabase Ġconsisting Ġof Ġaround Ġ117 k Ġconcepts , Ġwhich Ġare Ġorganized Ġinto Ġgroups Ġof Ġsy ns ets Ġthat Ġeach Ġcontain Ġa Ġgloss Ġ( i . e ., Ġa Ġdefinition Ġof Ġthe Ġtarget Ġconcept ), Ġa Ġset Ġof Ġrepresentative ĠEnglish Ġwords Ġ( called Ġle mm as ), Ġand , Ġin Ġaround Ġ33 k Ġsy ns ets , Ġexample Ġsentences . ĠIn Ġaddition , Ġmany Ġsy ns ets Ġhave ĠIS A Ġlinks Ġto Ġother Ġsy ns ets Ġthat Ġexpress Ġcomplex Ġtax onomic Ġrelations . ĠFigure ĠFIG REF 6 Ġshows Ġan Ġexample Ġand ĠTable ĠT AB REF 4 Ġsummarizes Ġhow Ġwe Ġformulate ĠWord Net Ġas Ġa Ġset Ġof Ġtri ples Ġ$\ math cal Ġ{ T } $ Ġof Ġvarious Ġtypes . ĠThese Ġtri ples Ġtogether Ġrepresent Ġa Ġdirected , Ġedge - label ed Ġgraph Ġ$ G $. ĠOur Ġmain Ġmotivation Ġfor Ġusing ĠWord Net , Ġas Ġopposed Ġto Ġa Ġresource Ġsuch Ġas ĠConcept Net ĠB IB REF 36 , Ġis Ġthe Ġavailability Ġof Ġgloss es Ġ($ \ math cal Ġ{ D } $ ) Ġand Ġexample Ġsentences Ġ($ \ math cal Ġ{ S } $ ), Ġwhich Ġallows Ġus Ġto Ġconstruct Ġnatural Ġlanguage Ġquestions Ġthat Ġcontextual ize Ġthe Ġtypes Ġof Ġconcepts Ġwe Ġwant Ġto Ġprobe . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠWord Net Q A Ġ: :: ĠExample ĠGeneration Ġ@ ! ST ART @ $ \ text sc Ġ{ gen } (\ t au Ġ) $ @ ! END @ . </s> We Ġbuild Ġ4 Ġindividual Ġdatasets Ġbased Ġon Ġsemantic Ġrelations Ġnative Ġto ĠWord Net Ġ( see ĠB IB REF 37 ): Ġhyper ny my Ġ( i . e ., Ġgeneral ization Ġor ĠIS A Ġreasoning Ġup Ġa Ġtax onomy , ĠIS A $ ^ \ up arrow Ġ$ ), Ġhyp ony my Ġ( ISA $ ^ {\ down arrow Ġ} $ ), Ġsyn ony my , Ġand Ġdefinitions . ĠTo Ġgenerate Ġa Ġset Ġof Ġquestions Ġin Ġeach Ġcase , Ġwe Ġemploy Ġa Ġnumber Ġof Ġrule Ġtemplates Ġ$\ math cal Ġ{ Q } $ Ġthat Ġoperate Ġover Ġtu ples . ĠA Ġsubset Ġof Ġsuch Ġtemplates Ġis Ġshown Ġin ĠTable ĠT AB REF 8 . ĠThe Ġtemplates Ġwere Ġdesigned Ġto Ġmimic Ġnatural istic Ġquestions Ġwe Ġobserved Ġin Ġour Ġscience Ġbenchmarks . </s> For Ġexample , Ġsuppose Ġwe Ġwish Ġto Ġcreate Ġa Ġquestion Ġ$\ text bf Ġ{ q } $ Ġabout Ġthe Ġdefinition Ġof Ġa Ġtarget Ġconcept Ġ$ c Ġ\ in Ġ\ math cal Ġ{ C } $. ĠWe Ġfirst Ġselect Ġa Ġquestion Ġtemplate Ġfrom Ġ$\ math cal Ġ{ Q } $ Ġthat Ġfirst Ġintroduces Ġthe Ġconcept Ġ$ c $ Ġand Ġits Ġle mma Ġ$ l Ġ\ in Ġ\ math cal Ġ{ W } $ Ġin Ġcontext Ġusing Ġthe Ġexample Ġsentence Ġ$ s Ġ\ in Ġ\ math cal Ġ{ S } $, Ġand Ġthen Ġasks Ġto Ġidentify Ġthe Ġcorresponding ĠWord Net Ġgloss Ġ$ d Ġ\ in Ġ\ math cal Ġ{ D } $, Ġwhich Ġserves Ġas Ġthe Ġgold Ġanswer Ġ$\ text bf Ġ{ a } $. ĠThe Ġsame Ġis Ġdone Ġfor ĠIS A Ġreasoning ; Ġeach Ġquestion Ġabout Ġa Ġhyper ny m / hyp onym Ġrelation Ġbetween Ġtwo Ġconcepts Ġ$ c Ġ\ right arrow Ġ^ {\ up arrow Ġ/ \ down arrow Ġ} Ġc ^ {\ prime Ġ} Ġ\ in Ġ\ math cal Ġ{ T } _{ i } $ Ġ( e . g ., Ġ$\ text tt Ġ{ dog } Ġ\ right arrow Ġ^ {\ up arrow Ġ/ \ down arrow Ġ} Ġ\ text tt Ġ{ animal / ter rier } $ ) Ġfirst Ġintroduces Ġa Ġcontext Ġfor Ġ$ c $ Ġand Ġthen Ġasks Ġfor Ġan Ġanswer Ġthat Ġidentifies Ġ$ c ^ {\ prime Ġ} $ Ġ( which Ġis Ġalso Ġprovided Ġwith Ġa Ġgloss Ġso Ġas Ġto Ġcontain Ġall Ġavailable Ġcontext ). </s> In Ġthe Ġlatter Ġcase , Ġthe Ġrules Ġ$( \ text tt Ġ{ isa } ^{ r }, c , c ^ {\ prime Ġ}) Ġ\ in Ġ\ math cal Ġ{ T } _ i $ Ġin ĠTable ĠT AB REF 8 Ġcover Ġonly Ġdirect ĠIS A Ġlinks Ġfrom Ġ$ c $ Ġin Ġdirection Ġ$ r Ġ\ in Ġ\ l brace Ġ\ up arrow Ġ, \ down arrow Ġ\ r brace Ġ$ . ĠIn Ġpractice , Ġfor Ġeach Ġ$ c $ Ġand Ġdirection Ġ$ r $, Ġwe Ġconstruct Ġtests Ġthat Ġcover Ġthe Ġset ĠH OPS $ ( c , r ) $ Ġof Ġall Ġdirect Ġas Ġwell Ġas Ġderived ĠIS A Ġrelations Ġof Ġ$ c $ : </s> This Ġallows Ġus Ġto Ġevaluate Ġthe Ġextent Ġto Ġwhich Ġmodels Ġare Ġable Ġto Ġhandle Ġcomplex Ġforms Ġof Ġreasoning Ġthat Ġrequire Ġseveral Ġinf erential Ġsteps Ġor Ġhops . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠWord Net Q A Ġ: :: ĠDist ractor ĠGeneration : Ġ@ ! ST ART @ $ \ text sc Ġ{ dist r } (\ t au Ġ^ {\ prime Ġ}) $ @ ! END @ . </s> An Ġexample Ġof Ġhow Ġdistract ors Ġare Ġgenerated Ġis Ġshown Ġin ĠFigure ĠFIG REF 6 , Ġwhich Ġrelies Ġon Ġsimilar Ġprinciples Ġas Ġabove . ĠFor Ġeach Ġconcept Ġ$ c $, Ġwe Ġchoose Ġ4 Ġdistract or Ġanswers Ġthat Ġare Ġclose Ġin Ġthe ĠWord Net Ġsemantic Ġspace . ĠFor Ġexample , Ġwhen Ġconstructing Ġhyper ny my Ġtests Ġfor Ġ$ c $ Ġfrom Ġthe Ġset Ġhops $ ( c , \ up arrow Ġ) $, Ġwe Ġbuild Ġdistract ors Ġby Ġdrawing Ġfrom Ġ$\ text sc Ġ{ hops } ( c , \ down arrow Ġ) $ Ġ( and Ġvice Ġversa ), Ġas Ġwell Ġas Ġfrom Ġthe Ġ$\ ell Ġ$ - deep Ġsister Ġfamily Ġof Ġ$ c $, Ġdefined Ġas Ġfollows . ĠThe Ġ1 - deep Ġsister Ġfamily Ġis Ġsimply Ġ$ c $ ' s Ġsiblings Ġor Ġsisters , Ġi . e ., Ġthe Ġother Ġchildren Ġ$\ t ilde { c } Ġ\ ne Ġc $ Ġof Ġthe Ġparent Ġnode Ġ$ c ^ {\ prime Ġ} $ Ġof Ġ$ c $. ĠFor Ġ$\ ell Ġ> Ġ1 $, Ġthe Ġ$\ ell Ġ$ - deep Ġsister Ġfamily Ġalso Ġincludes Ġall Ġdescendants Ġof Ġeach Ġ$\ t ilde { c } $ Ġup Ġto Ġ$\ ell Ġ- 1 $ Ġlevels Ġdeep , Ġden oted Ġ$\ text sc Ġ{ hops } _{ \ ell Ġ- 1 } (\ t ilde { c }, \ down arrow Ġ) $. ĠForm ally : </s> For Ġdefinitions Ġand Ġsyn onyms Ġwe Ġbuild Ġdistract ors Ġfrom Ġall Ġof Ġthese Ġsets Ġ( with Ġa Ġsimilar Ġrestriction Ġon Ġthe Ġdepth Ġof Ġsister Ġdistract ors Ġas Ġnoted Ġabove ). ĠIn Ġdoing Ġthis , Ġwe Ġcan Ġsystematically Ġinvestigate Ġmodel Ġperformance Ġon Ġa Ġwide Ġrange Ġof Ġdistract or Ġsets . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠWord Net Q A Ġ: :: ĠP ert urb ations Ġand ĠSem antic ĠCl usters </s> Based Ġon Ġhow Ġwe Ġgenerate Ġdata , Ġfor Ġeach Ġconcept Ġ$ c $ Ġ( i . e ., Ġatomic ĠWord Net Ġsyn set ) Ġand Ġprobe Ġtype Ġ( i . e ., Ġdefinitions , Ġhyper ny my , Ġetc .), Ġwe Ġhave Ġa Ġwide Ġvariety Ġof Ġquestions Ġrelated Ġto Ġ$ c $ Ġthat Ġmanipulate Ġ1 ) Ġthe Ġcomplexity Ġof Ġreasoning Ġthat Ġis Ġinvolved Ġ( e . g ., Ġthe Ġnumber Ġof Ġinf erential Ġhops ) Ġand ; Ġ2 ) Ġthe Ġtypes Ġof Ġdistract ors Ġ( or Ġdistract or Ġpert urb ations ) Ġthat Ġare Ġemployed . ĠWe Ġcall Ġsuch Ġsets Ġsemantic Ġclusters . ĠAs Ġwe Ġdescribe Ġin Ġthe Ġnext Ġsection , Ġsemantic Ġclusters Ġallow Ġus Ġto Ġdevise Ġnew Ġtypes Ġof Ġevaluation Ġthat Ġreveal Ġwhether Ġmodels Ġhave Ġcomprehensive Ġand Ġconsistent Ġknowledge Ġof Ġtarget Ġconcepts Ġ( e . g ., Ġevaluating Ġwhether Ġa Ġmodel Ġcan Ġcorrectly Ġanswer Ġseveral Ġquestions Ġassociated Ġwith Ġa Ġconcept , Ġas Ġopposed Ġto Ġa Ġfew Ġdis j oint Ġinstances ). </s> Details Ġof Ġthe Ġindividual Ġdatasets Ġare Ġshown Ġin ĠTable ĠT AB REF 12 . ĠFrom Ġthese Ġsets , Ġwe Ġfollow ĠB IB REF 22 Ġin Ġall ocating Ġa Ġmaximum Ġof Ġ3 k Ġexamples Ġfor Ġtraining Ġand Ġreserve Ġthe Ġrest Ġfor Ġdevelopment Ġand Ġtesting . ĠSince Ġwe Ġare Ġinterested Ġin Ġprobing , Ġhaving Ġlarge Ġheld - out Ġsets Ġallows Ġus Ġto Ġdo Ġdetailed Ġanalysis Ġand Ġcluster - based Ġevaluation . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠDictionary Q A </s> The ĠDictionary Q A Ġdataset Ġis Ġcreated Ġfrom Ġthe ĠGC IDE Ġdictionary , Ġwhich Ġis Ġa Ġcomprehensive Ġopen - source ĠEnglish Ġdictionary Ġbuilt Ġlargely Ġfrom Ġthe ĠWebster 's ĠRevised ĠUn ab rid ged ĠDictionary ĠB IB REF 38 . ĠEach Ġentry Ġconsists Ġof Ġa Ġword , Ġits Ġpart - of - speech , Ġits Ġdefinition , Ġand Ġan Ġoptional Ġexample Ġsentence Ġ( see ĠTable ĠT AB REF 14 ). ĠOverall , Ġ33 k Ġentries Ġ( out Ġof Ġa Ġtotal Ġof Ġ155 k ) Ġcontain Ġexample Ġsentences / us ages . ĠAs Ġwith Ġthe ĠWord Net Ġprobes , Ġwe Ġfocus Ġon Ġthis Ġsubset Ġso Ġas Ġto Ġcontextual ize Ġeach Ġword Ġbeing Ġprob ed . ĠIn Ġcontrast Ġto ĠWord Net , ĠGC IDE Ġdoes Ġnot Ġhave ĠIS A Ġrelations Ġor Ġexplicit Ġsy ns ets , Ġso Ġwe Ġtake Ġeach Ġunique Ġentry Ġto Ġbe Ġa Ġdistinct Ġsense . ĠWe Ġthen Ġuse Ġthe Ġdictionary Ġentries Ġto Ġcreate Ġa Ġprobe Ġthat Ġcenters Ġaround Ġword - sense Ġdis amb ig uation , Ġas Ġdescribed Ġbelow . </s> Dat as et ĠPro bes Ġand ĠConstruction Ġ: :: ĠDictionary Q A Ġ: :: ĠExample Ġand ĠDist ractor ĠGeneration . </s> To Ġgenerate Ġgold Ġquestions Ġand Ġanswers , Ġwe Ġuse Ġthe Ġsame Ġgeneration Ġtemplates Ġfor Ġdefinitions Ġexempl ified Ġin ĠFigure ĠT AB REF 8 Ġfor ĠWord Net Q A . ĠTo Ġgenerate Ġdistract ors , Ġwe Ġsimply Ġtake Ġalternative Ġdefinitions Ġfor Ġthe Ġtarget Ġwords Ġthat Ġrepresent Ġa Ġdifferent Ġword Ġsense Ġ( e . g ., Ġthe Ġalternative Ġdefinitions Ġof Ġgift Ġshown Ġin ĠTable ĠT AB REF 14 ), Ġas Ġwell Ġas Ġrandomly Ġchosen Ġdefinitions Ġif Ġneeded Ġto Ġcreate Ġa Ġ5 - way Ġmultiple Ġchoice Ġquestion . ĠAs Ġabove , Ġwe Ġreserve Ġa Ġmaximum Ġof Ġ3 k Ġexamples Ġfor Ġtraining . ĠSince Ġwe Ġhave Ġonly Ġ9 k Ġexamples Ġin Ġtotal Ġin Ġthis Ġdataset Ġ( see ĠWord Sense Ġin ĠTable ĠT AB REF 12 ), Ġwe Ġalso Ġreserve Ġ3 k Ġeach Ġfor Ġdevelopment Ġand Ġtesting . </s> We Ġnote Ġthat Ġinitial Ġattempts Ġto Ġbuild Ġthis Ġdataset Ġthrough Ġstandard Ġrandom Ġsplitting Ġgave Ġrise Ġto Ġcertain Ġsystematic Ġbiases Ġthat Ġwere Ġexploited Ġby Ġthe Ġchoice - only Ġbaseline Ġmodels Ġdescribed Ġin Ġthe Ġnext Ġsection , Ġand Ġhence Ġinflated Ġoverall Ġmodel Ġscores . ĠAfter Ġseveral Ġefforts Ġat Ġfiltering Ġwe Ġfound Ġthat , Ġamong Ġother Ġfactors , Ġusing Ġdefinitions Ġfrom Ġentries Ġwithout Ġexample Ġsentences Ġas Ġdistract ors Ġ( e . g ., Ġthe Ġfirst Ġtwo Ġentries Ġin ĠTable ĠT AB REF 14 ) Ġhad Ġa Ġsurprising Ġcorrelation Ġwith Ġsuch Ġbiases . ĠThis Ġsuggests Ġthat Ġpossible Ġbiases Ġinvolving Ġdifferences Ġbetween Ġdictionary Ġentries Ġwith Ġand Ġwithout Ġexamples Ġcan Ġt aint Ġthe Ġresulting Ġautomatically Ġgenerated ĠMC Q A Ġdataset Ġ( for Ġmore Ġdiscussion Ġon Ġthe Ġpitfalls Ġinvolved Ġwith Ġautomatic Ġdataset Ġconstruction , Ġsee ĠSection ĠSEC REF 5 ). </s> Pro bing ĠMethod ology Ġand ĠModel ing </s> Given Ġthe Ġprobes Ġabove , Ġwe Ġnow Ġcan Ġstart Ġto Ġanswer Ġthe Ġempirical Ġquestions Ġposed Ġat Ġthe Ġbeginning . ĠOur Ġmain Ġfocus Ġis Ġon Ġlooking Ġat Ġtransformer - based ĠMC Q A Ġmodels Ġtrained Ġin Ġthe Ġscience Ġdomain Ġ( using Ġthe Ġbenchmarks Ġshown Ġin ĠTable ĠT AB REF 21 ). ĠIn Ġthis Ġsection , Ġwe Ġprovide Ġdetails Ġof ĠMC Q A Ġand Ġthe Ġtarget Ġmodels , Ġas Ġwell Ġas Ġseveral Ġbas elines Ġthat Ġwe Ġuse Ġto Ġsanity Ġcheck Ġour Ġnew Ġdatasets . ĠTo Ġevaluate Ġmodel Ġcompetence , Ġwe Ġlook Ġat Ġa Ġcombination Ġof Ġmodel Ġperformance Ġafter Ġscience Ġpre - training Ġand Ġafter Ġadditional Ġmodel Ġfine - tun ing Ġusing Ġthe Ġloss less Ġinoc ulation Ġstrategy Ġof ĠB IB REF 22 Ġ( Section ĠSEC REF 22 ). ĠIn ĠSection ĠSEC REF 24 , Ġwe Ġalso Ġdiscuss Ġa Ġcluster - level Ġaccuracy Ġmetric Ġfor Ġmeasuring Ġperformance Ġover Ġsemantic Ġclusters . </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠTask ĠDefinition Ġand ĠModel ing </s> Given Ġa Ġdataset Ġ$ D Ġ= \ l brace Ġ( \ text bf Ġ{ q } ^{ ( d ) }, Ġ\ l brace Ġa _{ 1 } ^{ ( d ) }, ... , Ġa _{ N } ^{ ( d ) }\ r brace Ġ) Ġ\ r brace Ġ_ { d } ^ {\ mid ĠD Ġ\ mid Ġ} $ Ġconsisting Ġof Ġpairs Ġof Ġquestions Ġstems Ġ$\ text bf Ġ{ q } $ Ġand Ġanswer Ġchoices Ġ$ a _{ i } $, Ġthe Ġgoal Ġis Ġto Ġfind Ġthe Ġcorrect Ġanswer Ġ$ a _{ i ^{ * }} $ Ġthat Ġcorrectly Ġanswers Ġeach Ġ$\ text bf Ġ{ q } $. ĠThroughout Ġthis Ġpaper , Ġwe Ġlook Ġat Ġ5 - way Ġmultiple - choice Ġproblems Ġ( i . e ., Ġwhere Ġeach Ġ$ N = 5 $ ). </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠTask ĠDefinition Ġand ĠModel ing Ġ: :: ĠQuestion + Answer ĠEnc oder . </s> To Ġmodel Ġthis , Ġour Ġinvestigation Ġcenters Ġaround Ġthe Ġuse Ġof Ġthe Ġtransformer - based ĠB IB REF 25 ĠB ERT Ġenc oder Ġand Ġfine - tun ing Ġapproach Ġof ĠB IB REF 7 Ġ( see Ġalso ĠB IB REF 6 ). ĠFor Ġeach Ġquestion Ġand Ġindividual Ġanswer Ġpair Ġ$ q ^{ ( j )} _{ a _{ i }} $, Ġwe Ġassume Ġthe Ġfollowing Ġrendering Ġof Ġthis Ġinput : </s> which Ġis Ġrun Ġthrough Ġthe Ġpre - trained ĠB ERT Ġenc oder Ġto Ġgenerate Ġa Ġrepresentation Ġfor Ġ$ Ġq ^{ ( j )} _{ a _{ i }} $ Ġusing Ġthe Ġhidden Ġstate Ġrepresentation Ġfor ĠCLS Ġ( i . e ., Ġthe Ġclass ifier Ġtoken ) Ġ$\ text bf Ġ{ c } _{ i } $ : </s> The Ġprobability Ġof Ġa Ġgiven Ġanswer Ġ$ p ^{ ( j )} _{ i } $ Ġis Ġthen Ġcomputed Ġas Ġ$ p ^{ ( j )} _{ i } Ġ\ pro pt o Ġe ^ {\ text bf Ġ{ v }\ cd ot Ġ\ text bf Ġ{ c } ^{ ( j )} _{ i }} $, Ġwhich Ġuses Ġan Ġadditional Ġset Ġof Ġclassification Ġparameters Ġ$\ text bf Ġ{ v } Ġ\ in Ġ\ math bb Ġ{ R } ^{ H } $ Ġthat Ġare Ġoptimized Ġ( along Ġwith Ġthe Ġfull Ġtransformer Ġnetwork ) Ġby Ġtaking Ġthe Ġfinal Ġloss Ġof Ġthe Ġprobability Ġof Ġeach Ġcorrect Ġanswer Ġ$ p _{ i ^{ * }} $ Ġover Ġall Ġanswer Ġchoices : </s> We Ġspecifically Ġuse ĠB ERT - large Ġunc ased Ġwith Ġwhole - word Ġmask ing , Ġas Ġwell Ġas Ġthe ĠRo BER Ta - large Ġmodel Ġfrom ĠB IB REF 9 , Ġwhich Ġis Ġa Ġmore Ġrobust ly Ġtrained Ġversion Ġof Ġthe Ġoriginal ĠB ERT Ġmodel . ĠOur Ġsystem Ġuses Ġthe Ġimplementations Ġprovided Ġin ĠAllen N LP ĠB IB REF 39 Ġand ĠHug ging face ĠB IB REF 40 . </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠTask ĠDefinition Ġand ĠModel ing Ġ: :: ĠBas elines Ġand ĠSan ity ĠChecks . </s> When Ġcreating Ġsynthetic Ġdatasets , Ġit Ġis Ġimportant Ġto Ġensure Ġthat Ġsystematic Ġbiases , Ġor Ġannotation Ġartifacts ĠB IB REF 41 , Ġare Ġnot Ġintroduced Ġinto Ġthe Ġresulting Ġprobes Ġand Ġthat Ġthe Ġtarget Ġdatasets Ġare Ġsufficiently Ġchallenging Ġ( or Ġgood , Ġin Ġthe Ġsense Ġof ĠB IB REF 42 ). ĠTo Ġtest Ġfor Ġthis , Ġwe Ġuse Ġseveral Ġof Ġthe ĠMC Q A Ġbaseline Ġmodels Ġfirst Ġintroduced Ġin ĠB IB REF 0 , Ġwhich Ġtake Ġinspiration Ġfrom Ġthe ĠL ST M - based Ġmodels Ġused Ġin ĠB IB REF 43 Ġfor ĠNL I Ġand Ġvarious Ġpartial - input Ġbas elines Ġbased Ġon Ġthese Ġmodels . </s> Following Ġthe Ġnotation Ġfrom ĠB IB REF 0 , Ġfor Ġany Ġgiven Ġsequence Ġ$ s $ Ġof Ġtokens Ġin Ġ$\ l brace Ġq ^{ ( j ) }, Ġa _{ 1 } ^{ ( j ) }, ... , a _{ N } ^{ ( j ) }\ r brace Ġ$ Ġin Ġ$ D $, Ġan Ġencoding Ġof Ġ$ s $ Ġis Ġgiven Ġas Ġ$ h _{ s } ^{ ( j )} Ġ= Ġ\ text bf Ġ{ Bi L ST M } (\ text sc Ġ{ embed } ( s )) Ġ\ in Ġ\ math bb Ġ{ R } ^{ | s | Ġ\ times Ġ2 h } $ Ġ( where Ġ$ h $ Ġis Ġthe Ġdimension Ġof Ġthe Ġhidden Ġstate Ġin Ġeach Ġdirectional Ġnetwork , Ġand Ġembed $ (\ cd ot Ġ) $ Ġis Ġan Ġembed ding Ġfunction Ġthat Ġassigns Ġtoken - level Ġembed d ings Ġto Ġeach Ġtoken Ġin Ġ$ s $ ). ĠA Ġcontextual Ġrepresentation Ġfor Ġeach Ġ$ s $ Ġis Ġthen Ġbuilt Ġby Ġapplying Ġan Ġelement - wise Ġmax Ġoperation Ġover Ġ$ h _{ s } $ Ġas Ġfollows : </s> With Ġthese Ġcontextual Ġrepresentations , Ġdifferent Ġbaseline Ġmodels Ġcan Ġbe Ġconstructed . ĠFor Ġexample , Ġa ĠChoice - Only Ġmodel , Ġwhich Ġis Ġa Ġvariant Ġof Ġthe Ġwell - known Ġhypothesis - only Ġbaseline Ġused Ġin ĠNL I ĠB IB REF 46 , Ġscores Ġeach Ġchoice Ġ$ c _{ i } $ Ġin Ġthe Ġfollowing Ġway : </s> for Ġ$\ text bf Ġ{ W } ^{ T } Ġ\ in Ġ\ math bb Ġ{ R } ^{ 2 h } $ Ġindependently Ġof Ġthe Ġquestion Ġand Ġassigns Ġa Ġprobability Ġto Ġeach Ġanswer Ġ$ p _{ i } ^{ ( j )} Ġ\ pro pt o Ġe ^ {\ alpha Ġ_ { i } ^{ ( j ) }} $. </s> A Ġslight Ġvariant Ġof Ġthis Ġmodel , Ġthe ĠChoice - to - choice Ġmodel , Ġtries Ġto Ġsingle Ġout Ġa Ġgiven Ġanswer Ġchoice Ġrelative Ġto Ġother Ġchoices Ġby Ġscoring Ġall Ġchoice Ġpairs Ġ$\ alpha Ġ_ { i , i ^ {\ prime Ġ}} ^{ ( j )} Ġ= Ġ\ text sc Ġ{ Att } ( r ^{ ( j )} _{ c _{ i } }, r ^{ ( j )} _{ c _{ i ^ {\ prime Ġ} }} ) Ġ\ in Ġ\ math bb Ġ{ R } $ Ġusing Ġa Ġlearned Ġattention Ġmechanism ĠAtt Ġand Ġfinding Ġthe Ġchoice Ġwith Ġthe Ġminimal Ġsimilarity Ġto Ġother Ġoptions Ġ( for Ġfull Ġdetails , Ġsee Ġtheir Ġoriginal Ġpaper ). ĠIn Ġusing Ġthese Ġpartial - input Ġbas elines , Ġwhich Ġwe Ġtrain Ġdirectly Ġon Ġeach Ġtarget Ġprobe , Ġwe Ġcan Ġcheck Ġwhether Ġsystematic Ġbiases Ġrelated Ġto Ġanswer Ġchoices Ġwere Ġintroduced Ġinto Ġthe Ġdata Ġcreation Ġprocess . </s> A ĠQuestion - to - choice Ġmodel , Ġin Ġcontrast , Ġuses Ġthe Ġcontextual Ġrepresentations Ġfor Ġeach Ġquestion Ġand Ġindividual Ġchoice Ġand Ġan Ġattention Ġmodel ĠAtt Ġmodel Ġto Ġget Ġa Ġscore Ġ$\ alpha Ġ^ { ( j )} _{ q , i } Ġ= Ġ\ text sc Ġ{ Att } ( r ^{ ( j )} _{ q }, r ^{ ( j )} _{ c _{ i }} ) Ġ\ in Ġ\ math bb Ġ{ R } $ Ġas Ġabove . ĠHere Ġwe Ġalso Ġexperiment Ġwith Ġusing ĠES IM ĠB IB REF 47 Ġto Ġgenerate Ġthe Ġcontextual Ġrepresentations Ġ$ r $, Ġas Ġwell Ġas Ġa Ġsimpler ĠVec Similar ity Ġmodel Ġthat Ġmeasures Ġthe Ġaverage Ġvector Ġsimilarity Ġbetween Ġquestion Ġand Ġanswer Ġtokens : Ġ$\ alpha Ġ^ { ( j )} _{ q , i } Ġ= Ġ\ text sc Ġ{ Sim } (\ text sc Ġ{ embed } ( q ^{ ( j )} ), \ text sc Ġ{ embed } ( c ^{ ( j )} _{ i } )) $. ĠIn Ġcontrast Ġto Ġthe Ġmodels Ġabove , Ġthese Ġsets Ġof Ġbas elines Ġare Ġused Ġto Ġcheck Ġfor Ġartifacts Ġbetween Ġquestions Ġand Ġanswers Ġthat Ġare Ġnot Ġcaptured Ġin Ġthe Ġpartial - input Ġbas elines Ġ( see Ġdiscussion Ġin ĠB IB REF 49 ) Ġand Ġensure Ġthat Ġthe Ġoverall ĠMC Q A Ġtasks Ġare Ġsufficiently Ġdifficult Ġfor Ġour Ġtransformer Ġmodels . </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠIn oc ulation Ġand ĠPre - training </s> Using Ġthe Ġvarious Ġmodels Ġintroduced Ġabove , Ġwe Ġtrain Ġthese Ġmodels Ġon Ġbenchmark Ġtasks Ġin Ġthe Ġscience Ġdomain Ġand Ġlook Ġat Ġmodel Ġperformance Ġon Ġour Ġprobes Ġwith Ġand Ġwithout Ġadditional Ġtraining Ġon Ġsamples Ġof Ġprobe Ġdata , Ġbuilding Ġon Ġthe Ġidea Ġof Ġinoc ulation Ġfrom ĠB IB REF 23 . ĠModel Ġinoc ulation Ġis Ġthe Ġidea Ġof Ġcontinuing Ġto Ġtrain Ġmodels Ġon Ġnew Ġchallenge Ġtasks Ġ( in Ġour Ġcases , Ġseparately Ġfor Ġeach Ġprobe ) Ġusing Ġonly Ġa Ġsmall Ġamount Ġof Ġexamples . ĠUnlike Ġin Ġordinary Ġfine - tun ing , Ġthe Ġgoal Ġis Ġnot Ġto Ġlearn Ġan Ġentirely Ġre - pur posed Ġmodel , Ġbut Ġto Ġimprove Ġon Ġ( or Ġvacc inate Ġagainst ) Ġparticular Ġphenomena Ġ( e . g ., Ġour Ġsynthetic Ġprobes ) Ġthat Ġpotentially Ġdev iate Ġfrom Ġa Ġmodel 's Ġoriginal Ġtraining Ġdistribution Ġ( but Ġthat Ġnonetheless Ġmight Ġinvolve Ġknowledge Ġalready Ġcontained Ġin Ġthe Ġmodel ). </s> In Ġthe Ġvariant Ġproposed Ġin ĠB IB REF 22 , Ġfor Ġeach Ġpre - trained Ġ( science ) Ġmodel Ġand Ġarchitecture Ġ$ M _{ a } $ Ġwe Ġcontinue Ġtraining Ġthe Ġmodel Ġon Ġ$ k $ Ġnew Ġprobe Ġexamples Ġ( with Ġa Ġmaximum Ġof Ġ$ k =$ Ġ3 k ) Ġunder Ġa Ġset Ġof Ġdifferent Ġhyper - param eter Ġconfigurations Ġ$ j Ġ\ in Ġ\ l brace Ġ1 , Ġ... , ĠJ \ r brace Ġ$ Ġand Ġidentify , Ġfor Ġeach Ġ$ k $, Ġthe Ġmodel Ġ$ M _{ * } ^{ a , k } $ Ġwith Ġthe Ġbest Ġaggregate Ġperformance Ġ$ S $ Ġon Ġthe Ġoriginal Ġ( orig ) Ġand Ġnew Ġtask : </s> As Ġin ĠB IB REF 22 , Ġwe Ġfound Ġall Ġmodels Ġto Ġbe Ġespecially Ġsensitive Ġto Ġdifferent Ġlearning Ġrates , Ġand Ġperformed Ġcomprehensive Ġhyper - param eters Ġsearches Ġthat Ġalso Ġmanipulate Ġthe Ġnumber Ġof Ġiterations Ġand Ġrandom Ġseeds Ġused . </s> Using Ġthis Ġmethodology , Ġwe Ġcan Ġsee Ġhow Ġmuch Ġexposure Ġto Ġnew Ġdata Ġit Ġtakes Ġfor Ġa Ġgiven Ġmodel Ġto Ġmaster Ġa Ġnew Ġtask , Ġand Ġwhether Ġthere Ġare Ġphenomena Ġthat Ġstress Ġparticular Ġmodels Ġ( e . g ., Ġlead Ġto Ġcatastrophic Ġforgetting Ġof Ġthe Ġoriginal Ġtask ). ĠGiven Ġthe Ġrestrictions Ġon Ġthe Ġnumber Ġof Ġfine - tun ing Ġexamples , Ġour Ġassumption Ġis Ġthat Ġwhen Ġmodels Ġare Ġable Ġto Ġmaintain Ġgood Ġperformance Ġon Ġtheir Ġoriginal Ġtask Ġduring Ġinoc ulation , Ġthe Ġquick ness Ġwith Ġwhich Ġthey Ġare Ġable Ġto Ġlearn Ġthe Ġinoc ulated Ġtask Ġprovides Ġevidence Ġof Ġprior Ġcompetence , Ġwhich Ġis Ġprecisely Ġwhat Ġwe Ġaim Ġto Ġprobe . ĠTo Ġmeasure Ġpast Ġperformance , Ġwe Ġdefine Ġa Ġmodel 's Ġinoc ulation Ġcost Ġas Ġthe Ġdifference Ġin Ġthe Ġperformance Ġof Ġthis Ġmodel Ġon Ġits Ġoriginal Ġtask Ġbefore Ġand Ġafter Ġinoc ulation . </s> We Ġpre - train Ġon Ġan Ġaggreg ated Ġtraining Ġset Ġof Ġthe Ġbenchmark Ġscience Ġexams Ġdetailed Ġin ĠTable ĠT AB REF 21 , Ġand Ġcreated Ġan Ġaggregate Ġdevelopment Ġset Ġof Ġaround Ġ4 k Ġscience Ġquestions Ġfor Ġevaluating Ġoverall Ġscience Ġperformance Ġand Ġinoc ulation Ġcosts . ĠTo Ġhandle Ġthe Ġmismatch Ġbetween Ġnumber Ġof Ġanswer Ġchoices Ġin Ġthese Ġsets , Ġwe Ġmade Ġall Ġsets Ġ5 - way Ġby Ġadding Ġempty Ġanswers Ġas Ġneeded . ĠWe Ġalso Ġexperimented Ġwith Ġa Ġslight Ġvariant Ġof Ġinoc ulation , Ġcalled Ġadd - some Ġinoc ulation , Ġwhich Ġinvolves Ġbalancing Ġthe Ġinoc ulation Ġtraining Ġsets Ġwith Ġnatural istic Ġscience Ġquestions . ĠWe Ġreserve Ġthe ĠMC QL Ġdataset Ġin ĠTable ĠT AB REF 21 Ġfor Ġthis Ġpurpose , Ġand Ġexperiment Ġwith Ġbalancing Ġeach Ġprobe Ġexample Ġwith Ġa Ġscience Ġexample Ġ( x 1 Ġmatching ) Ġand Ġadding Ġtwice Ġas Ġmany Ġscience Ġquestions Ġ( x 2 Ġmatching , Ġup Ġto Ġ3 k ) Ġfor Ġeach Ġnew Ġexample . </s> Pro bing ĠMethod ology Ġand ĠModel ing Ġ: :: ĠEval uating ĠModel ĠCompet ence </s> The Ġstandard Ġway Ġto Ġevaluate Ġour ĠMC Q A Ġmodels Ġis Ġby Ġlooking Ġat Ġthe Ġoverall Ġaccuracy Ġof Ġthe Ġcorrect Ġanswer Ġprediction , Ġor Ġwhat Ġwe Ġcall Ġinstance - level Ġaccuracy Ġ( as Ġin ĠTable ĠT AB REF 25 ). ĠGiven Ġthe Ġnature Ġof Ġour Ġdata Ġand Ġthe Ġexistence Ġof Ġsemantic Ġclusters Ġas Ġdetailed Ġin ĠSection ĠSEC REF 11 Ġ( i . e ., Ġsets Ġof Ġquestions Ġand Ġanswers Ġunder Ġdifferent Ġdistract or Ġchoices Ġand Ġinference Ġcomplexity ), Ġwe Ġalso Ġmeasure Ġa Ġmodel 's Ġcluster - level Ġ( or Ġstrict Ġcluster ) Ġaccuracy , Ġwhich Ġrequires Ġcorrectly Ġanswering Ġall Ġquestions Ġin Ġa Ġcluster . ĠExample Ġsemantic Ġclusters Ġare Ġshown Ġin ĠTable ĠT AB REF 30 ; Ġin Ġthe Ġfirst Ġcase , Ġthere Ġare Ġ6 ĠIS A $ ^ \ up arrow Ġ$ Ġquestions Ġ( including Ġpert urb ations ) Ġabout Ġthe Ġconcept Ġtrou ser . n . 01 Ġ( e . g ., Ġinvolving Ġknowing Ġthat Ġtrousers Ġare Ġa Ġtype Ġof Ġconsumer Ġgood Ġand Ġgarment / cl othing ), Ġwhich Ġa Ġmodel Ġmust Ġanswer Ġin Ġorder Ġto Ġreceive Ġfull Ġcredit . </s> Our Ġcluster - based Ġanalysis Ġis Ġmotivated Ġby Ġthe Ġidea Ġthat Ġif Ġa Ġmodel Ġtruly Ġknows Ġthe Ġmeaning Ġof Ġa Ġgiven Ġconcept , Ġsuch Ġas Ġthe Ġconcept Ġof Ġtrousers , Ġthen Ġit Ġshould Ġbe Ġable Ġto Ġanswer Ġarbitrary Ġquestions Ġabout Ġthis Ġconcept Ġwithout Ġsensitivity Ġto Ġvaried Ġdistract ors . ĠWhile Ġour Ġstrict Ġcluster Ġmetric Ġis Ġsimplistic , Ġit Ġtakes Ġinspiration Ġfrom Ġwork Ġon Ġvisual ĠQ A ĠB IB REF 53 , Ġand Ġallows Ġus Ġto Ġevaluate Ġhow Ġconsistent Ġand Ġrobust Ġmodels Ġare Ġacross Ġour Ġdifferent Ġprobes , Ġand Ġto Ġget Ġinsight Ġinto Ġwhether Ġerrors Ġare Ġconcentrated Ġon Ġa Ġsmall Ġset Ġof Ġconcepts Ġor Ġwidespread Ġacross Ġclusters . </s> Results Ġand ĠFind ings </s> In Ġthis Ġsection , Ġwe Ġprovide Ġthe Ġresults Ġof Ġthe Ġempirical Ġquestions Ġfirst Ġintroduced Ġin ĠFigure ĠFIG REF 1 , Ġstarting Ġwith Ġthe Ġresults Ġof Ġour Ġbaseline Ġmodels . </s> Results Ġand ĠFind ings Ġ: :: ĠAre Ġour ĠPro bes ĠS ufficient ly ĠChall eng ing ? </s> As Ġshown Ġin ĠTable ĠT AB REF 25 , Ġmost Ġof Ġour Ġpartial - input Ġbas elines Ġ( i . e ., ĠChoice - Only Ġand ĠChoice - to - Choice Ġmodels ) Ġfailed Ġto Ġperform Ġwell Ġon Ġour Ġdataset Ġprobes Ġacross Ġa Ġwide Ġrange Ġof Ġmodels , Ġshowing Ġthat Ġsuch Ġprobes Ġare Ġgenerally Ġimmune Ġfrom Ġbiases Ġrelating Ġto Ġhow Ġdistract ors Ġwere Ġgenerated . ĠAs Ġalready Ġdiscussed Ġin ĠSection ĠSEC REF 13 , Ġhowever , Ġinitial Ġversions Ġof Ġthe ĠDictionary Q A Ġdataset Ġhad Ġunforeseen Ġbiases Ġpartly Ġrelated Ġto Ġwhether Ġdistract ors Ġwere Ġsampled Ġfrom Ġentries Ġwithout Ġexample Ġsentences , Ġwhich Ġresulted Ġin Ġhigh ĠChoice - Only - G lo Ve Ġscores Ġranging Ġaround Ġ56 % Ġaccuracy Ġbefore Ġa Ġfiltering Ġstep Ġwas Ġapplied Ġto Ġremove Ġthese Ġdistract ors . </s> We Ġhad Ġsimilar Ġissues Ġwith Ġthe Ġhyper ny my Ġprobe Ġwhich , Ġeven Ġafter Ġa Ġfiltering Ġstep Ġthat Ġused Ġour ĠChoice - to - Choice - G lo Ve Ġmodel , Ġstill Ġleads Ġto Ġhigh Ġresults Ġon Ġthe ĠB ERT Ġand ĠRo BER Ta Ġchoice - only Ġmodels . ĠGiven Ġthat Ġseveral Ġattempts Ġwere Ġmade Ġto Ġentirely Ġde - du pl icate Ġthe Ġdifferent Ġsplits Ġ( both Ġin Ġterms Ġof Ġgold Ġanswers Ġand Ġdistract or Ġtypes ), Ġthe Ġsource Ġof Ġthese Ġbiases Ġis Ġnot Ġat Ġall Ġobvious , Ġwhich Ġshows Ġhow Ġeasy Ġit Ġis Ġfor Ġunintended Ġbiases Ġin Ġexpert Ġknowledge Ġto Ġappear Ġin Ġthe Ġresulting Ġdatasets Ġand Ġthe Ġimportance Ġof Ġhaving Ġrigorous Ġbas elines . ĠWe Ġalso Ġnote Ġthe Ġlarge Ġgap Ġin Ġsome Ġcases Ġbetween Ġthe ĠB ERT Ġand ĠRo BER Ta Ġversus ĠGl o Ve Ġchoice - only Ġmodels , Ġwhich Ġhighlights Ġthe Ġneed Ġfor Ġhaving Ġpartial - input Ġbas elines Ġthat Ġuse Ġthe Ġbest Ġavailable Ġmodels . </s> Using Ġa Ġmore Ġconventional Ġset Ġof ĠTask - Specific ĠQ A Ġmodels Ġ( i . e ., Ġthe ĠL ST M - based ĠQuestion - to - Choice Ġmodels Ġtrained Ġdirectly Ġon Ġthe Ġprobes ), Ġwe Ġcan Ġsee Ġthat Ġresults Ġare Ġnot Ġparticularly Ġstrong Ġon Ġany Ġof Ġthe Ġdatasets , Ġsuggesting Ġthat Ġour Ġprobes Ġare Ġindeed Ġsufficiently Ġchallenging Ġand Ġlargely Ġimmune Ġfrom Ġovert Ġartifacts . ĠThe Ġpoor Ġperformance Ġof Ġthe ĠVec Similar ity Ġ( which Ġuses Ġpre - trained ĠWord 2 V ec Ġembed d ings Ġwithout Ġadditional Ġtraining ) Ġprovides Ġadditional Ġevidence Ġthat Ġelementary Ġlex ical Ġmatching Ġstrategies Ġare Ġinsufficient Ġfor Ġsolving Ġany Ġof Ġthe Ġprobing Ġtasks . </s> Results Ġand ĠFind ings Ġ: :: ĠHow Ġwell Ġdo Ġpre - trained ĠMC Q A Ġmodels Ġdo ? </s> Science Ġmodels Ġthat Ġuse Ġnon - trans former Ġbased Ġenc od ers , Ġsuch Ġas Ġthe ĠES IM Ġmodel Ġwith ĠGl o Ve Ġand ĠEL MO , Ġperform Ġpoorly Ġacross Ġall Ġprobes , Ġin Ġmany Ġcases Ġscoring Ġnear Ġrandom Ġchance , Ġshowing Ġlimits Ġto Ġhow Ġwell Ġthey Ġgeneral ize Ġfrom Ġscience Ġto Ġother Ġtasks Ġeven Ġwith Ġpre - trained ĠGl o Ve Ġand ĠEL MO Ġembed d ings . ĠIn Ġsharp Ġcontrast , Ġthe Ġtransformer Ġmodels Ġhave Ġmixed Ġresults , Ġthe Ġmost Ġstriking Ġresult Ġbeing Ġthe ĠRo BER Ta Ġmodels Ġon Ġthe Ġdefinitions Ġand Ġsyn ony my Ġprobes Ġ( ach ieving Ġa Ġtest Ġaccuracy Ġof Ġ77 % Ġand Ġ61 %, Ġrespectively ), Ġwhich Ġoutper form Ġseveral Ġof Ġthe Ġtask - specific ĠL ST M Ġmodels Ġtrained Ġdirectly Ġon Ġthe Ġprobes . ĠAt Ġfirst Ġglance , Ġthis Ġsuggests Ġthat ĠRo BER Ta , Ġwhich Ġgenerally Ġfar Ġoutp aces Ġeven ĠB ERT Ġacross Ġmost Ġprobes , Ġhas Ġhigh Ġcompetence Ġof Ġdefinitions Ġand Ġsyn onyms Ġeven Ġwithout Ġexplicit Ġtraining Ġon Ġour Ġnew Ġtasks . </s> Given Ġthe Ġcontrolled Ġnature Ġof Ġour Ġprobes , Ġwe Ġcan Ġget Ġa Ġmore Ġdetailed Ġview Ġof Ġhow Ġwell Ġthe Ġscience Ġmodels Ġare Ġperforming Ġacross Ġdifferent Ġreasoning Ġand Ġdistract or Ġtypes , Ġas Ġshown Ġin Ġthe Ġfirst Ġcolumn Ġof ĠFigure ĠFIG REF 28 Ġfor ĠES IM Ġand ĠRo BER Ta . ĠThe ĠES IM Ġscience Ġmodel Ġwithout Ġtraining Ġhas Ġuniformly Ġpoor Ġperformance Ġacross Ġall Ġcategories , Ġwhereas Ġthe Ġperformance Ġof ĠRo BER Ta Ġis Ġmore Ġvaried . ĠAcross Ġall Ġdatasets Ġand Ġnumber Ġof Ġhops Ġ( i . e ., Ġthe Ġrows Ġin Ġthe Ġheat Ġmaps ), Ġmodel Ġperformance Ġfor ĠRo BER Ta Ġis Ġconsistently Ġhighest Ġamong Ġexamples Ġwith Ġrandom Ġdistract ors Ġ( i . e ., Ġthe Ġfirst Ġcolumn ), Ġand Ġlowest Ġin Ġcases Ġinvolving Ġdistract ors Ġthat Ġare Ġclosest Ġin ĠWord Net Ġspace Ġ( e . g ., Ġsister Ġand ĠIS A , Ġor Ġup / down , Ġdistract ors Ġof Ġdistance Ġ$ k ^ {\ prime Ġ} = 1 $ ). ĠThis Ġis Ġnot Ġsurprising , Ġgiven Ġthat , Ġin Ġthe Ġfirst Ġcase , Ġrandom Ġdistract ors Ġare Ġlikely Ġto Ġbe Ġthe Ġeasiest Ġcategory Ġ( and Ġthe Ġopposite Ġfor Ġdistract ors Ġclose Ġin Ġspace ), Ġbut Ġsuggests Ġthat ĠRo BER Ta Ġmight Ġonly Ġbe Ġgetting Ġthe Ġeasiest Ġcases Ġcorrect . </s> Model Ġperformance Ġalso Ġclearly Ġde grades Ġfor Ġhyper ny my Ġand Ġhyp ony my Ġacross Ġall Ġmodels Ġas Ġthe Ġnumber Ġof Ġhops Ġ$ k $ Ġincreases Ġ( see Ġred Ġdashed Ġboxes ). ĠFor Ġexample , Ġproblems Ġthat Ġinvolve Ġhyp onym Ġreasoning Ġwith Ġsister Ġdistract ors Ġof Ġdistance Ġ$ k ^ {\ prime Ġ} = 1 $ Ġ( i . e ., Ġthe Ġsecond Ġcolumn ) Ġde grades Ġfrom Ġ47 % Ġto Ġ15 % Ġwhen Ġthe Ġnumber Ġof Ġhops Ġ$ k $ Ġincreases Ġfrom Ġ1 Ġto Ġ4 . ĠThis Ġgeneral Ġtendency Ġpersists Ġeven Ġafter Ġadditional Ġfine - tun ing , Ġas Ġwe Ġdiscuss Ġnext , Ġand Ġgives Ġevidence Ġthat Ġmodels Ġare Ġlimited Ġin Ġtheir Ġcapacity Ġfor Ġcertain Ġtypes Ġof Ġmulti - hop Ġinf erences . </s> As Ġdiscussed Ġby ĠB IB REF 26 , Ġthe Ġchoice Ġof Ġgeneration Ġtemplates Ġcan Ġhave Ġa Ġsignificant Ġeffect Ġon Ġmodel Ġperformance . ĠThe Ġresults Ġso Ġfar Ġshould Ġtherefore Ġbe Ġregarded Ġas Ġa Ġlower Ġbound Ġon Ġmodel Ġcompetence . ĠIt Ġis Ġpossible Ġthat Ġmodel Ġperformance Ġis Ġhigh Ġfor Ġdefinitions , Ġfor Ġexample , Ġbecause Ġthe Ġassociated Ġtemplates Ġbest Ġalign Ġwith Ġthe Ġscience Ġtraining Ġdistribution Ġ( which Ġwe Ġknow Ġlittle Ġabout ). ĠFor Ġthis Ġreason , Ġthe Ġsubsequent Ġinoc ulation Ġstep Ġis Ġimportant âĢĶ it Ġgives Ġthe Ġmodel Ġan Ġopportunity Ġto Ġlearn Ġabout Ġour Ġtarget Ġtemplates Ġand Ġcouple Ġthis Ġlearned Ġknowledge Ġwith Ġits Ġgeneral Ġknowledge Ġacquired Ġduring Ġpre - training Ġand Ġscience Ġtraining Ġ( which Ġis , Ġagain , Ġwhat Ġwe Ġaim Ġto Ġprobe ). </s> Results Ġand ĠFind ings Ġ: :: ĠCan ĠModels ĠBe ĠEffect ively ĠIn oc ulated ? </s> Model Ġperformance Ġafter Ġadditional Ġfine - tun ing , Ġor Ġinoc ulation , Ġis Ġshown Ġin Ġthe Ġlast Ġ3 Ġrows Ġof ĠTable ĠT AB REF 25 , Ġalong Ġwith Ġlearning Ġcurves Ġshown Ġin ĠFigure ĠFIG REF 29 Ġfor Ġa Ġselection Ġof Ġprobes Ġand Ġmodels . ĠIn Ġthe Ġformer Ġcase , Ġthe Ġperformance Ġrepresents Ġthe Ġmodel Ġ( and Ġinoc ulation Ġamount ) Ġwith Ġthe Ġhighest Ġaggregate Ġperformance Ġover Ġthe Ġold Ġtask Ġand Ġnew Ġprobe . ĠHere Ġwe Ġagain Ġsee Ġthe Ġtransformer - based Ġmodels Ġoutper form Ġnon - trans former Ġmodels , Ġand Ġthat Ġbetter Ġmodels Ġcorrelate Ġwith Ġlower Ġinoc ulation Ġcosts . ĠFor Ġexample , Ġwhen Ġinoc ulating Ġon Ġsyn ony my , Ġthe Ġcost Ġfor ĠES IM Ġis Ġaround Ġ7 % Ġreduced Ġaccuracy Ġon Ġits Ġoriginal Ġtask , Ġas Ġopposed Ġto Ġ$ < Ġ1 $ % Ġand Ġaround Ġ1 % Ġfor ĠB ERT Ġand ĠRo BER Ta , Ġrespectively . ĠThis Ġshows Ġthe Ġhigh Ġcapacity Ġfor Ġtransformer Ġmodels Ġto Ġabsorb Ġnew Ġtasks Ġwith Ġminimal Ġcosts , Ġas Ġalso Ġobserved Ġin ĠB IB REF 22 Ġfor ĠNL I . </s> As Ġshown Ġin ĠFigure ĠFIG REF 29 , Ġtransformer Ġmodels Ġtend Ġto Ġlearn Ġmost Ġtasks Ġfairly Ġquickly Ġwhile Ġkeeping Ġconstant Ġscores Ġon Ġtheir Ġoriginal Ġtasks Ġ( i . e ., Ġthe Ġflat Ġdashed Ġlines Ġobserved Ġin Ġplots Ġ1 - 4 ), Ġwhich Ġgives Ġevidence Ġof Ġhigh Ġcompetence . ĠIn Ġboth Ġcases , Ġadd - some Ġinoc ulation Ġproves Ġto Ġbe Ġa Ġcheap Ġand Ġeasy Ġway Ġto Ġ1 ) Ġimprove Ġscores Ġon Ġthe Ġprobing Ġtasks Ġ( i . e ., Ġthe Ġsolid Ġblack Ġand Ġblue Ġlines Ġin Ġplot Ġ1 ) Ġand ; Ġ2 ) Ġminimize Ġloss Ġon Ġscience Ġ( e . g ., Ġthe Ġblue Ġand Ġblack Ġdashed Ġlines Ġin Ġplots Ġ2 - 4 ). ĠThe Ġopposite Ġis Ġthe Ġcase Ġfor ĠES IM Ġ( pl ots Ġ5 - 6 ); Ġmodels Ġare Ġgenerally Ġunable Ġto Ġsimultaneously Ġlearn Ġindividual Ġprobes Ġwithout Ġdegrading Ġon Ġtheir Ġoriginal Ġtask , Ġand Ġadding Ġmore Ġscience Ġdata Ġduring Ġinoc ulation Ġconf uses Ġmodels Ġon Ġboth Ġtasks . </s> As Ġshown Ġin ĠFigure ĠFIG REF 28 , ĠRo BER Ta Ġis Ġable Ġto Ġsignificantly Ġimprove Ġperformance Ġacross Ġmost Ġcategories Ġeven Ġafter Ġinoc ulation Ġwith Ġa Ġmere Ġ100 Ġexamples Ġ( the Ġmiddle Ġplot ), Ġwhich Ġagain Ġprovides Ġstrong Ġevidence Ġof Ġprior Ġcompetence . ĠAs Ġan Ġexample , ĠRo BER Ta Ġimproves Ġon Ġ2 - hop Ġhyp ony my Ġinference Ġwith Ġrandom Ġdistract ors Ġby Ġ18 % Ġ( from Ġ59 % Ġto Ġ77 %). ĠAfter Ġ3 k Ġexamples , Ġthe Ġmodel Ġhas Ġhigh Ġperformance Ġon Ġvirtually Ġall Ġcategories Ġ( the Ġsame Ġscore Ġincreases Ġfrom Ġ59 % Ġto Ġ87 %), Ġhowever Ġresults Ġstill Ġtends Ġto Ġdegrade Ġas Ġa Ġfunction Ġof Ġhop Ġand Ġdistract or Ġcomplexity , Ġas Ġdiscussed Ġabove . </s> Despite Ġthe Ġhigh Ġperformance Ġof Ġour Ġtransformer Ġmodels Ġafter Ġinoc ulation , Ġmodel Ġperformance Ġon Ġmost Ġprobes Ġ( with Ġthe Ġexception Ġof ĠDefinitions ) Ġaverages Ġaround Ġ80 % Ġfor Ġour Ġbest Ġmodels . ĠThis Ġsuggests Ġthat Ġthere Ġis Ġstill Ġconsiderable Ġroom Ġfor Ġimprovement , Ġespecially Ġfor Ġsyn ony my Ġand Ġword Ġsense , Ġwhich Ġis Ġa Ġtopic Ġthat Ġwe Ġdiscuss Ġmore Ġin ĠSection ĠSEC REF 6 . </s> Results Ġand ĠFind ings Ġ: :: ĠAre ĠModels ĠCons istent Ġacross ĠCl usters ? </s> Table ĠT AB REF 32 Ġshows Ġcluster - level Ġaccur acies Ġfor Ġthe Ġdifferent ĠWord Net Q A Ġprobes . ĠAs Ġwith Ġperformance Ġacross Ġthe Ġdifferent Ġinference / dist ractor Ġcategories , Ġthese Ġresults Ġare Ġmixed . ĠFor Ġsome Ġprobes , Ġsuch Ġas Ġdefinitions , Ġour Ġbest Ġmodels Ġappear Ġto Ġbe Ġrather Ġrobust ; Ġe . g ., Ġour ĠRo BER Ta Ġmodel Ġhas Ġa Ġcluster Ġaccuracy Ġof Ġ$ 75 \ % $, Ġmeaning Ġthat Ġit Ġcan Ġanswer Ġall Ġquestions Ġperfectly Ġfor Ġ75 % Ġof Ġthe Ġtarget Ġconcepts Ġand Ġthat Ġerrors Ġare Ġconcentrated Ġon Ġa Ġsmall Ġminority Ġ( 25 %) Ġof Ġconcepts . ĠOn Ġsyn ony my Ġand Ġhyper ny my , Ġboth ĠB ERT Ġand ĠRo BER Ta Ġappear Ġrobust Ġon Ġthe Ġmajority Ġof Ġconcepts , Ġshowing Ġthat Ġerrors Ġare Ġsimilarly Ġconcentrated . ĠIn Ġcontrast , Ġour Ġbest Ġmodel Ġon Ġhyp ony my Ġhas Ġan Ġaccuracy Ġof Ġ36 %, Ġmeaning Ġthat Ġits Ġerrors Ġare Ġspread Ġacross Ġmany Ġconcepts , Ġthus Ġsuggesting Ġless Ġrobust ness . </s> Table ĠT AB REF 30 Ġshows Ġa Ġselection Ġof Ġsemantic Ġclusters Ġinvolving ĠIS A Ġreasoning , Ġas Ġwell Ġas Ġthe Ġmodel Ġperformance Ġover Ġdifferent Ġanswers Ġ( shown Ġsymbol ically ) Ġand Ġpert urb ations . ĠFor Ġexample , Ġin Ġthe Ġthe Ġsecond Ġcase , Ġthe Ġcluster Ġis Ġbased Ġaround Ġthe Ġconcept / syn set Ġoppose . v . 06 Ġand Ġinvolves Ġ4 Ġinf erences Ġand Ġa Ġtotal Ġ24 Ġquestions Ġ( i . e ., Ġinf erences Ġwith Ġpert urb ations ). ĠOur Ġweakest Ġmodel , ĠES IM , Ġanswers Ġonly Ġ5 Ġout Ġof Ġ24 Ġquestions Ġcorrectly , Ġwhereas ĠRo BER Ta Ġgets Ġ21 / 24 . ĠIn Ġthe Ġother Ġcases , ĠRo BER Ta Ġgets Ġall Ġclusters Ġcorrect , Ġwhereas ĠB ERT Ġand ĠES IM Ġget Ġnone Ġof Ġthem Ġcorrect . </s> We Ġemphasize Ġthat Ġthese Ġresults Ġonly Ġprovide Ġa Ġcrude Ġlook Ġinto Ġmodel Ġconsistency Ġand Ġrobust ness . ĠRec alling Ġagain Ġthe Ġdetails Ġin ĠTable ĠT AB REF 12 , Ġprobes Ġdiffer Ġin Ġterms Ġof Ġaverage Ġsize Ġof Ġclusters . ĠHyp ony my , Ġin Ġvirtue Ġof Ġhaving Ġmany Ġmore Ġquestions Ġper Ġcluster , Ġmight Ġsimply Ġbe Ġa Ġmuch Ġmore Ġdifficult Ġdataset . ĠIn Ġaddition , Ġsuch Ġa Ġstrict Ġevaluation Ġdoes Ġnot Ġtake Ġinto Ġaccount Ġpotential Ġerrors Ġinside Ġof Ġclusters , Ġwhich Ġis Ġan Ġimportant Ġissue Ġthat Ġwe Ġdiscuss Ġin Ġthe Ġnext Ġsection . ĠWe Ġleave Ġaddressing Ġsuch Ġissues Ġand Ġcoming Ġup Ġwith Ġmore Ġinsightful Ġcluster - based Ġmetrics Ġfor Ġfuture Ġwork . </s> Discussion Ġand ĠConclusion </s> We Ġpresented Ġseveral Ġnew Ġchallenge Ġdatasets Ġand Ġa Ġnovel Ġmethodology Ġfor Ġautomatically Ġbuilding Ġsuch Ġdatasets Ġfrom Ġknowledge Ġgraphs Ġand Ġtax onom ies . ĠWe Ġused Ġthese Ġto Ġprobe Ġstate - of - the - art Ġopen - domain ĠQ A Ġmodels Ġ( cent ering Ġaround Ġmodels Ġbased Ġon Ġvariants Ġof ĠB ERT ). ĠWhile Ġour Ġgeneral Ġmethodology Ġis Ġamend able Ġto Ġany Ġtarget Ġknowledge Ġresource Ġor ĠQ A Ġmodel / domain , Ġwe Ġfocus Ġon Ġprobing Ġdefinitions Ġand ĠIS A Ġknowledge Ġusing Ġopen - source Ġdiction aries Ġand ĠMC Q A Ġmodels Ġtrained Ġin Ġthe Ġscience Ġdomain . </s> We Ġfind , Ġconsistent Ġwith Ġrecent Ġprobing Ġstudies ĠB IB REF 26 , Ġthat Ġtransformer - based Ġmodels Ġhave Ġa Ġremarkable Ġability Ġto Ġanswer Ġquestions Ġthat Ġinvolve Ġcomplex Ġforms Ġof Ġrelational Ġknowledge , Ġboth Ġwith Ġand Ġwithout Ġexplicit Ġexposure Ġto Ġour Ġnew Ġtarget Ġtasks . ĠIn Ġthe Ġlatter Ġcase , Ġa Ġnewer ĠRo BER Ta Ġmodel Ġtrained Ġonly Ġon Ġbenchmark Ġscience Ġtasks Ġis Ġable Ġto Ġoutper form Ġseveral Ġtask - specific ĠL ST M - based Ġmodels Ġtrained Ġdirectly Ġon Ġour Ġprobing Ġdata . ĠWhen Ġre - trained Ġon Ġsmall Ġsamples Ġ( e . g ., Ġ100 Ġexamples ) Ġof Ġprobing Ġdata Ġusing Ġvariations Ġof Ġthe Ġloss less Ġinoc ulation Ġstrategy Ġfrom ĠB IB REF 22 , ĠRo BER Ta Ġis Ġable Ġto Ġmaster Ġmany Ġaspects Ġof Ġour Ġprobes Ġwith Ġvirtually Ġno Ġperformance Ġloss Ġon Ġits Ġoriginal ĠQ A Ġtask . </s> These Ġpositive Ġresults Ġsuggest Ġthat Ġtransformer - based Ġmodels , Ġespecially Ġmodels Ġadditionally Ġfine - tun ed Ġon Ġsmall Ġsamples Ġof Ġsynthetic Ġdata , Ġcan Ġbe Ġused Ġin Ġplace Ġof Ġtask - specific Ġmodels Ġused Ġfor Ġquer ying Ġrelational Ġknowledge , Ġas Ġhas Ġalready Ġbeen Ġdone Ġfor Ġtargeted Ġtasks Ġsuch Ġas Ġword Ġsense Ġdis amb ig uation ĠB IB REF 54 . ĠSince Ġmodels Ġseem Ġto Ġalready Ġcontain Ġconsiderable Ġamounts Ġof Ġrelational Ġknowledge , Ġour Ġsimple Ġinoc ulation Ġstrategy , Ġwhich Ġtries Ġto Ġn udge Ġmodels Ġto Ġbring Ġout Ġthis Ġknowledge Ġexplicitly , Ġcould Ġserve Ġas Ġa Ġcheaper Ġalternative Ġto Ġrecent Ġattempts Ġto Ġbuild Ġarchitectures Ġthat Ġexplicitly Ġincorporate Ġstructured Ġknowledge ĠB IB REF 55 ; Ġwe Ġsee Ġmany Ġareas Ġwhere Ġour Ġinoc ulation Ġstrategy Ġcould Ġbe Ġimproved Ġfor Ġsuch Ġpurposes , Ġincluding Ġhaving Ġmore Ġcomplex Ġloss Ġfunctions Ġthat Ġmanage Ġold Ġand Ġnew Ġinformation , Ġas Ġwell Ġas Ġusing Ġtechniques Ġthat Ġtake Ġinto Ġaccount Ġnetwork Ġplastic ity ĠB IB REF 56 . </s> The Ġmain Ġappeal Ġof Ġusing Ġautomatically Ġgenerate Ġdatasets Ġis Ġthe Ġability Ġto Ġsystematically Ġmanipulate Ġand Ġcontrol Ġthe Ġcomplexity Ġof Ġtarget Ġquestions , Ġwhich Ġallows Ġfor Ġmore Ġcontrolled Ġexperimentation Ġand Ġnew Ġforms Ġof Ġevaluation . ĠDespite Ġthe Ġpositive Ġresults Ġdescribed Ġabove , Ġresults Ġthat Ġlook Ġdirectly Ġat Ġthe Ġeffect Ġof Ġdifferent Ġtypes Ġof Ġdistract ors Ġand Ġthe Ġcomplexity Ġof Ġreasoning Ġshow Ġthat Ġour Ġbest Ġmodels , Ġeven Ġafter Ġadditional Ġfine - tun ing , Ġstruggle Ġwith Ġcertain Ġcategories Ġof Ġhard Ġdistract ors Ġand Ġmulti - hop Ġinf erences . ĠFor Ġsome Ġprobes , Ġour Ġcluster - based Ġanalysis Ġalso Ġreveals Ġthat Ġerrors Ġare Ġwidespread Ġacross Ġconcept Ġclusters , Ġsuggesting Ġthat Ġmodels Ġare Ġnot Ġalways Ġconsistent Ġand Ġrobust . ĠThese Ġresults , Ġtaken Ġtogether Ġwith Ġour Ġfindings Ġabout Ġthe Ġvulnerability Ġof Ġsynthetic Ġdatasets Ġto Ġsystematic Ġbiases , Ġsuggest Ġthat Ġthere Ġis Ġmuch Ġroom Ġfor Ġimprovement Ġand Ġthat Ġthe Ġpositive Ġresults Ġshould Ġbe Ġtaken Ġwith Ġa Ġgrain Ġof Ġsalt . ĠDevelop ing Ġbetter Ġways Ġto Ġevaluate Ġsemantic Ġclusters Ġand Ġmodel Ġrobust ness Ġwould Ġbe Ġa Ġstep Ġin Ġthis Ġdirection . </s> We Ġemphasize Ġthat Ġusing Ġsynthetic Ġversus Ġnatural istic ĠQ A Ġdata Ġcomes Ġwith Ġimportant Ġtrade - offs . ĠWhile Ġwe Ġare Ġable Ġto Ġgenerate Ġlarge Ġamounts Ġof Ġsystematically Ġcontrolled Ġdata Ġat Ġvirtually Ġno Ġcost Ġor Ġneed Ġfor Ġmanual Ġannotation , Ġit Ġis Ġmuch Ġharder Ġto Ġvalidate Ġthe Ġquality Ġof Ġsuch Ġdata Ġat Ġsuch Ġa Ġscale Ġand Ġsuch Ġvarying Ġlevels Ġof Ġcomplexity . ĠConversely , Ġwith Ġbenchmark ĠQ A Ġdatasets , Ġit Ġis Ġmuch Ġharder Ġto Ġperform Ġthe Ġtype Ġof Ġcareful Ġmanip ulations Ġand Ġcluster - based Ġanalyses Ġwe Ġreport Ġhere . ĠWhile Ġwe Ġassume Ġthat Ġthe Ġexpert Ġknowledge Ġwe Ġemploy , Ġin Ġvirtue Ġof Ġbeing Ġhand - cur ated Ġby Ġhuman Ġexperts , Ġis Ġgenerally Ġcorrect , Ġwe Ġknow Ġthat Ġsuch Ġresources Ġare Ġfall ible Ġand Ġerror - prone . ĠInitial Ġcrowd - s ourcing Ġexperiments Ġthat Ġlook Ġat Ġvalid ating Ġsamples Ġof Ġour Ġdata Ġshow Ġhigh Ġagreement Ġacross Ġprobes Ġand Ġthat Ġhuman Ġscores Ġcorrelate Ġwith Ġthe Ġmodel Ġtrends Ġacross Ġthe Ġprobe Ġcategories . ĠMore Ġdetails Ġof Ġthese Ġstudies Ġare Ġleft Ġfor Ġfuture Ġwork .