Datasets:
| Fact | or Fiction: | Verifying | Scientific | Claims | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DavidWadden†∗ | ShanchuanLin† | KyleLo‡ | LucyLuWang‡ | |||||||||
| MadeleinevanZuylen‡ | ArmanCohan‡ | HannanehHajishirzi†‡ | ||||||||||
| † UniversityofWashington,Seattle,WA,USA | ||||||||||||
| ‡ | ||||||||||||
| AllenInstituteforArtificialIntelligence,Seattle,WA,USA | ||||||||||||
| {dwadden,linsh,hannaneh}@cs.washington.edu | ||||||||||||
| {kylel,lucyw,madeleinev,armanc}@allenai.org | ||||||||||||
| Abstract | ||||||||||||
| Claim | ||||||||||||
| We introduce scientific claim verification, a Cardiac injury is common in | ||||||||||||
| new task to select abstracts from the re- critical cases of COVID-19. | ||||||||||||
| searchliteraturecontainingevidencethatSUP- | ||||||||||||
| PORTS | or REFUTES | a given | scientific | claim, | Fact-checker | |||||||
| ----- | ---------- | --- | ------- | ---------- | ------ | --- | --- | --- | ------------ | --- | --- | --- |
| Corpus | ||||||||||||
| and to | identify | rationales | justifying | each de- | ||||||||
| ------- | -------- | ---------- | ----- | ------------ | -------- | --- | --- | --- | --------- | --- | -------- | --- |
| Decision: | SUPPORTS | |||||||||||
| cision. | To study | this | task, | we construct | SCI- | |||||||
| More severe COVID-19 infection | ||||||||||||
| FACT, | a dataset | of | 1.4K expert-written | scien- | ||||||||
| ------------ | --------- | ------ | ------------------------ | --- | ------ | --- | --- | ------------------------- | --- | ----------------------- | --- | ----- |
| is associated with higher | mean | |||||||||||
| tific claims | paired | with evidence-containing | ||||||||||
| troponin | (SMD 0.53, 95% CI 0.30 | |||||||||||
| abstracts annotated with labels and rationales. to 0.75, p < 0.001) | ||||||||||||
| WedevelopbaselinemodelsforSCIFACT,and | Rationale | |||||||||||
| ------------------------------------- | --- | ---- | ------------- | --- | ---------- | ------ | --------------- | --- | ------ | --------- | ----------- | --- |
| demonstrate | that | simple domain | adaptation | |||||||||
| Figure | 1: A scientific | claim, | supported | by evidence | ||||||||
| techniquessubstantiallyimproveperformance | ||||||||||||
| compared to models trained on Wikipedia or identifiedbyoursystem. Tocorrectlyverifythisclaim, | ||||||||||||
| the system | must | possess | background | knowledge | that | |||||||
| --------- | ------ | ------ | ------- | ----------- | --------- | ---------- | ------------ | ------- | ---------- | ------- | ---------- | ---- |
| political | news. | We | show | that our | system is | |||||||
| troponin | is a protein | found | in | cardiac | muscle and | that | ||||||
| able to | verify | claims | related | to COVID-19 | by | |||||||
| identifying evidence from the CORD-19 cor- elevated levels of troponin are a marker of cardiac | ||||||||||||
| injury. | In addition, | it | must be | able to | reason about | di- | ||||||
| ---- | --------------- | --- | -------- | ---- | ------- | ------- | ------------ | --- | ------- | ------- | ------------ | --- |
| pus. | Our experiments | indicate | that | SCIFACT | ||||||||
| will provide a challenging testbed for the de- rectionalrelationshipsbetweenscientificprocesses: re- | ||||||||||||
| placing | higher | with | lower would | cause | the rationale | |||||||
| --- | --- | --- | --- | --- | --- | ------- | ------ | ---- | ----------- | ----- | ------------- | --- |
| velopmentofnewsystemsdesignedtoretrieve | ||||||||||||
| to REFUTE | theclaimratherthan | SUPPORT | it. | Finally, | ||||||||
| --- | --- | --- | --- | --- | --- | --------- | ------------------ | --- | --- | ------- | --- | -------- |
| andreasonovercorporacontainingspecialized | ||||||||||||
| domain knowledge. Data and code for this thesystemshouldinterpret p<0.001asanindication | ||||||||||||
| thatthereportedfindingisstatisticallysignificant. | ||||||||||||
| new task | are | publicly | available | at | https:// | |||||||
| --------------------------- | --- | -------- | ------------- | --- | --------- | --- | --- | --- | --- | --- | --- | --- |
| github.com/allenai/scifact. | A leader- | |||||||||||
| board | and | COVID-19 | fact-checking | demo | ||||||||
| are available at https://scifact.apps. Fact-checking – a task in which the veracity | ||||||||||||
| allenai.org. | ||||||||||||
| of an input | claim | is | verified | against | a corpus | of | ||||||
| --- | --- | --- | --- | --- | --- | ----------- | ----- | ------- | -------- | ------- | --------- | ----- |
| documents | that | support | or | refute | the claim | – has | ||||||
| 1 Introduction | ||||||||||||
| beenstudiedtocombattheproliferationofmisin- | ||||||||||||
| Due to rapid growth in the scientific literature, it formationinpoliticalnews, socialmedia, andon | ||||||||||||
| is difficult for researchers – and the general pub- the web (Thorne et al., 2018; Hanselowski et al., | ||||||||||||
| 2019). However,verifyingscientificclaimsposes | ||||||||||||
| licevenmoreso–tostayuptodateonthelatest | ||||||||||||
| findings. Thischallengeisespeciallyacuteduring new challenges to both dataset construction and | ||||||||||||
| publichealthcriseslikethecurrentCOVID-19pan- effectivemodeling. Whilepoliticalclaimsareread- | ||||||||||||
| ilyavailableonfact-checkingwebsitesandcanbe | ||||||||||||
| demic,duetotheextremelyfastrateatwhichnew | ||||||||||||
| findingsarereportedandtherisksassociatedwith verifiedbycrowdworkers,annotatorswithexten- | ||||||||||||
| makingdecisionsbasedonoutdatedorincomplete sive domain knowledge are required to generate | ||||||||||||
| andverifyscientificclaims. | ||||||||||||
| information. | As | a result, | there | is a need | for auto- | |||||||
| ------------ | --- | --------- | ----- | --------- | --------- | --- | --- | --- | --- | --- | --- | --- |
| matedtoolstoassistresearchersandthepublicin In addition, NLP systems for scientific claim | ||||||||||||
| evaluatingtheveracityofscientificclaims. verificationmustpossessadditionalcapabilitiesbe- | ||||||||||||
| yond those | required | to verify | factoid | claims. | For | |||||||
| --- | --- | --- | --- | --- | --- | ---------- | -------- | --- | --------- | ------- | ------- | --- |
| ∗WorkperformedduringinternshipwiththeAllenInsti- | ||||||||||||
| tuteforArtificialIntelligence. instance,toverifytheclaimshowninFigure1,a | ||||||||||||
| 7534 | ||||||||||||
| Proceedingsofthe2020ConferenceonEmpiricalMethodsinNaturalLanguageProcessing,pages7534–7550, | ||||||||||||
| November16–20,2020.(cid:13)c2020AssociationforComputationalLinguistics |
Claim1:Lopinavir/ritonavirhaveexhibitedfavorableclinicalresponseswhenusedasatreatmentforcoronavirus. Supports:...Interestingly,afterlopinavir/ritonavir(Kaletra,AbbVie)wasadministered,β-coronavirusviralloadssignificantly decreasedandnoorlittlecoronavirustiterswereobserved. Refutes:Thefocuseddrugrepurposingofknownapproveddrugs(suchaslopinavir/ritonavir)hasbeenreportedfailedfor curingSARS-CoV-2infectedpatients.Itisurgenttogeneratenewchemicalentitiesagainstthisvirus... Claim2:Thecoronaviruscannotthriveinwarmerclimates. Supports:...mostoutbreaksdisplayapatternofclusteringinrelativelycoolanddryareas...Thisisbecausetheenvironment canmediatehuman-to-humantransmissionofSARS-CoV-2,andunsuitableclimatescancausethevirustodestabilizequickly... Refutes: ...significantcasesinthecomingmonthsarelikelytooccurinmorehumid(warmer)climates,irrespectiveofthe climate-dependenceoftransmissionandthatsummertemperatureswillnotsubstrantiallylimitpandemicgrowth. Table1: EvidenceidentifiedbyoursystemassupportingandrefutingtwoclaimsconcerningCOVID-19. system must have the ability to access scientific CORD-19corpus(Wangetal.,2020). Expertanno- backgroundknowledge,reasonoverincreasesand tatorsjudgeretrievedevidencetobeplausiblefor 23of36claims.1 decreasesinquantitiesormeasurements,andmake Ourresultsandanalysesdemon- senseofspecializedstatisticallanguage. stratetheimportanceofthenewtaskanddatasetto In this paper, we introduce the task of scien- supportsignificantfutureresearchinthisdomain. tific claim verification to evaluate the veracity of Insummary,ourcontributionsinclude: (1)We scientific claims against a scientific corpus. Ta- introduceandformalizethescientificclaimverifi- ble 1 presents some examples. To facilitate re- cation task. (2) We develop a novel annotation search on this task, we construct SCIFACT, an protocol to generate and verify 1.4K naturally- expert-annotateddatasetof1,409scientificclaims occurringclaimsaboutscientificfindings. (3)We accompanied by abstracts that support or refute establish strong baselines on this task, and iden- eachclaim,andannotatedwithrationales(Leietal., tify substantial opportunities for improvement at 2016)justifyingeachSUPPORTS/REFUTESdeci- allstagesofthemodelingpipeline. (4)Wedemon- sion. Tocreatethedataset,wedevelopanovelan- stratetheefficacyofoursysteminareal-worldcase notationprotocolinwhichannotatorsre-formulate studyverifyingclaimsaboutCOVID-19againstthe
| naturallyoccurringclaimsinthescientificliterature | researchliterature. | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| –citationsentences–intoatomicscientificclaims. | ||||||||||
| 2 Backgroundandtaskdefinition | ||||||||||
| --- | --- | --- | --- | --- | --- | ----------------------------- | --- | --- | --- | --- |
| Usingcitationsentencesasasourceofclaimsboth | ||||||||||
| speedstheclaimgenerationprocessandguarantees | ||||||||||
| As illustrated | in Figure | 1, | scientific claim | verifi- | ||||||
| ------------------------ | --- | --- | ------------------- | --- | --- | -------------- | --------- | --- | ---------------- | ------- |
| thatthetopicsdiscussedin | SCIFACTarerepresen- | |||||||||
| cationisthetaskofidentifyingevidencefromthe | ||||||||||
| tativeoftheresearchliterature. | Inaddition,citation | |||||||||
| ------------------------------ | --- | --- | ------------------- | --- | --- | ---------------------- | --- | -------- | --- | --------- |
| researchliteraturethat | SUPPORTS | or | REFUTES a | |||||||
| linksindicatetheexactdocumentslikelytocontain | ||||||||||
| given scientific | claim. | Table | 1 shows | the results | ||||||
| --- | --- | --- | --- | --- | --- | ---------------- | ------ | ----- | ------- | ----------- |
| evidencenecessarytoverifyagivenclaim. | ||||||||||
| of our system | applied | to | claims about | the novel | ||||||
| --- | --- | --- | --- | --- | --- | ------------- | ------- | --- | ------------ | --------- |
| WeestablishperformancebaselinesonSCIFACT | ||||||||||
| coronavirus | COVID-19. | For each claim, | the sys- | |||||||
| --- | --- | --- | --- | --- | --- | ----------- | --------- | --- | --------------- | -------- |
| withanapproachsimilartoDeYoungetal.(2020a), | ||||||||||
| temidentifiesrelevantscientificabstracts,andla- | ||||||||||
| whichachievesstrongperformanceontheFEVER | ||||||||||
| bels the | relation of | each | abstract to the | claim as | ||||||
| ------------------------------------------- | ------------- | ------ | ----- | --------- | --- | ------------------------ | ----------- | ---- | ------------------- | -------- |
| claimverificationdataset(Thorneetal.,2018). | Our | |||||||||
| eitherSUPPORTSorREFUTES. | Verifyingscientific | |||||||||
| baseline | is a pipeline | system | which | retrieves | ab- | |||||
| claimsischallengingandrequiresdomain-specific | ||||||||||
| stracts | related | to an input | claim, uses | a | BERT- | |||||
| ------- | ------- | ----------- | ----------- | --- | ----- | --- | --- | --- | --- | --- |
| backgroundknowledge–forinstance,inorderto | ||||||||||
| based(Devlinetal.,2019)sentenceselectortoiden- | ||||||||||
| identify | the evidence | supporting | Claim | 1 in Ta- | ||||||
| -------------- | ---------- | --- | ---------- | ------------- | --- | -------- | ------------ | ---------- | ----- | -------- |
| tify rationale | sentences, | and labels | each abstract | |||||||
| ble1,thesystemmustdeterminethatareductionin | ||||||||||
| as SUPPORTS, | REFUTES,or | NOINFO | withrespect | |||||||
| ------------ | ---------- | --- | ------ | ----------- | --- | --- | --- | --- | --- | --- |
| coronavirusviralloadindicatesafavorableclinical | ||||||||||
| to the claim. | We | demonstrate | that | our baseline | ||||||
| ------------- | --- | ----------- | ---- | ------------ | --- | --- | --- | --- | --- | --- |
| response,eventhoughthisfactisnevermentioned. | ||||||||||
| canbenefitfromtrainingonclaimsfromdomains | ||||||||||
| ScientificclaimsIn | SCIFACT,ascientificclaimis | |||||||||
| --- | --- | --- | --- | --- | --- | ------------------ | --- | -------------------------- | --- | --- |
| includingWikipediaarticlesandpolitics. | ||||||||||
| We showcase the ability of our model to ver- anatomicverifiablestatementexpressingafinding | ||||||||||
| ify expert-written | claims | concerning | the | novel | ||||||
| ------------------ | --- | ------ | ---------- | --- | ----- | --- | --- | --- | --- | --- |
| 1Weemphasizethatourmodelisaresearchprototypeand | ||||||||||
| coronavirusCOVID-19againstthenewly-released shouldnotbeusedtomakeanymedicaldecisionswhatsoever. | ||||||||||
| 7535 |
| about one | aspect | of a | scientific | entity | or | process, | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Corpus | |||||||||||||
| source.2 | N=601 | ||||||||||||
| --------- | ----------- | --- | ---- | -------- | -------- | --- | --- | ----- | --- | --- | --- | --- | --- |
| which can | be verified | from | a single | For | |||||||||
| Citing | |||||||||||||
| instance,“TheR | ofthenovelcoronavirusis2.5” | ||||||||||||
| -------------- | --- | --------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | |||||||||||||
| is valid, | but opinion-based | statements | like | “The | Seed | ||||||||
| --------- | ----------------- | --- | --- | ---------- | ---- | ---- | --- | --- | ---- | --- | --- | --- | --- |
| governmentshouldrequirepeopletostandsixfeet | |||||||||||||
| N=140 | |||||||||||||
| apart to | stop coronavirus” | are not. | Compound | ||||||||||
| -------- | ----------------- | --- | --- | -------- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
| claimslike“Aerosolizedcoronavirusdropletscan | |||||||||||||
| travelatleast6feetandcanremainintheairfor3 | Co- | ||||||||||||
| ------------------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| cited | |||||||||||||
| hours”shouldbesplitintotwoatomicclaims. | |||||||||||||
| Claim | |||||||||||||
| Claims | in SCIFACT | are | natural | – they | are de- | N=4,259 | |||||||
| ------ | ---------- | --- | --- | ------- | ------ | ------- | --- | ------- | --- | --- | --- | --- | --- |
| rivedfromcitationsentences,orcitances(Nakov Cardiac injury is | |||||||||||||
| common in critical | |||||||||||||
| et al., 2004), | that | occur | naturally | in | scientific | ar- | Dis- | ||||||
| -------------- | ---- | ----- | --------- | --- | ---------- | --- | --- | --- | ---- | --- | --- | --- | --- |
| cases of COVID-19. | |||||||||||||
| tractor | |||||||||||||
| ticles. This | is | similar | to political | fact-checking | |||||||||
| ------------ | --- | ------- | ------------ | --- | ------------- | --- | --- | --- | --- | --- | --- | --- | --- |
| datasetssuchasUKPSnopes(Hanselowskietal., | |||||||||||||
| 2019),whichusepoliticalfact-checkingwebsites | |||||||||||||
| Figure | 2: | Corpus construction. | Citing | abstracts | are | ||||||||
| --- | --- | --- | --- | --- | --- | --- | ------ | --- | -------------------- | --- | ------ | --------- | --- |
| as a source of natural claims. On the other hand, identified for each seed document. A claim is written | |||||||||||||
| basedonthesourcecitanceinthecitingabstract. | |||||||||||||
| claimsinthepopular | FEVERdataset(Thorneetal., | ||||||||||||
| ------------------ | --- | --- | ------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 2018)aresynthetic,sincetheyarecreatedbyanno- | |||||||||||||
| tators by | mutating | sentences | from | the Wikipedia | |||||||||
| --------- | -------- | --------- | --- | ---- | ------------- | --- | --- | --- | --- | --- | --- | --- | --- |
| Abstractsthatsupportorrefuteeachclaimarean- | |||||||||||||
| articlesthatwillserveasevidence. | |||||||||||||
| notated | with | rationales. | We | describe | our | corpus | |||||||
| ---------- | --- | -------- | --- | -------- | ------- | ----- | ------- | ---- | ----------- | --- | -------- | --- | ------ |
| Supporting | and | refuting | evidence | In most | fact- | ||||||||
| creationandannotationprocess. | |||||||||||||
| checkingwork,claimsareassignedaglobaltruth | |||||||||||||
| labelbasedontheentiretyoftheavailableevidence. 3.1 Datasourceandcorpusconstruction | |||||||||||||
| ForexampleinFEVER,theclaim“BarackObama | |||||||||||||
| Toconstruct | SCIFACT,weuseS2ORC(Loetal., | ||||||||||||
| ---------- | -------------------------------- | --- | --- | --- | --- | --- | ----------- | --- | --------------------------- | --- | ------ | ----------- | --- |
| wasthe44th | PresidentoftheUnitedStates”canbe | ||||||||||||
| 2020), | a | publicly-available | corpus | of millions | of | ||||||||
| verifiedusingWikipediaasanevidencesource. | |||||||||||||
| scientificarticles. | Toensurethatdocumentsinour | ||||||||||||
| ----- | ------- | ---------------------------- | --- | --- | --- | --- | ------------------- | --- | -------------------------- | --- | ----------- | --- | ------ |
| While | SCIFACT | claimsareindeedverifiableas- | |||||||||||
| dataset | are | of high quality, | we randomly | sample | |||||||||
| sertionsaboutscientificfindings,accuratelyassign- | |||||||||||||
| articlesfromamanuallycuratedcollectionofwell- | |||||||||||||
| ingaglobaltruthlabeltoascientificclaim(givena | |||||||||||||
| regardedjournalsspanningdomainsfrombasicsci- | |||||||||||||
| fixedscientificcorpus)requiresasystematicreview ence(e.g.,Cell,Nature)toclinicalmedicine(e.g., | |||||||||||||
| byateamofexperts. | Inthisworkwefocusonthe | ||||||||||||
| ----------------- | --- | --- | ---------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| JAMA,BMJ).Thefulllistofjournalsisincludedin | |||||||||||||
| simplertaskofassigning | SUPPORTSor | REFUTES | |||||||||||
| ---------------------- | --- | --- | ---------- | --- | ------- | --- | ------------ | --- | ------------------------------- | --- | --- | --- | --- |
| AppendixC.1. | Werestricttoarticleswithatleast | ||||||||||||
| relationstoindividualclaim-abstractpairs. 10 citations. The resulting collection is referred | |||||||||||||
| Each SUPPORTS or REFUTESrelationbetween to as our seed set. We use the S2ORC citation | |||||||||||||
| claimandabstractmustbejustifiedbyatleastone | |||||||||||||
| graph | to | sample source | citances | from | citing | arti- | |||||||
| ---------- | ----------- | --- | ---- | ------- | ---------- | --- | ------------------------------- | --- | ------------- | -------- | --------------- | ------ | ----- |
| rationale. | A rationale | is a | minimal | collection | of | ||||||||
| cleswhichcitetheseseedarticles. | Ifacitancecites | ||||||||||||
| sentenceswhich,takentogetheraspremisesinthe otherarticlesnotintheseedset,werefertothese | |||||||||||||
| contextoftheabstract,canreasonablybejudgedby | |||||||||||||
| asco-cited | articlesandaddthemtothecorpus,as | ||||||||||||
| -------------------------------- | --- | --- | --- | --- | ---------- | --- | ------------------ | --- | -------------------------------- | ----------------------- | --- | --- | --- |
| adomainexpertasimplyingtheclaim. | Rationales | ||||||||||||
| depictedinFigure2. | Thecontentofthecitedab- | ||||||||||||
| facilitatethedevelopmentofinterpretablemodels stractsencompassesadiversearrayoftopicswithin | |||||||||||||
| whichnotonlyhavetheabilitytomakelabelpre- biomedicine,asshowninFigure3. Themajority | |||||||||||||
| dictions,butcanalsoidentifytheexactsentences | |||||||||||||
| of | citances | used for | SCIFACT | cite | only | the seed | |||||||
| --- | --- | --- | --- | --- | --- | --- | --- | -------- | -------- | ------- | ---- | ---- | -------- |
| thatarenecessaryfortheirdecisions. | |||||||||||||
| article(noco-citedarticles),aswefoundininitial | |||||||||||||
| annotationexperimentsthatthesecitancestended | |||||||||||||
| 3 The | SCIFACT | dataset | |||||||||||
| ----- | ------- | ------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| toyieldspecific,easy-to-verifyclaims. | |||||||||||||
| To | expand | the corpus, | we | identify | five | papers | |||||||
| ----------- | --- | ------- | -------- | -------- | ---------- | --- | --- | ------ | ----------- | --- | -------- | ---- | ------ |
| The SCIFACT | dataset | consists | of 1,409 | scientific | |||||||||
| claims3 verifiedagainstacorpusof5,183abstracts. citedinthesamepaperaseachsourcecitancebut | |||||||||||||
| inadifferentparagraph,andaddthesetothecor- | |||||||||||||
| 2Requiringannotatorstosearchmultiplesourcesincreases pusasdistractorabstracts. Theseabstractsoften | |||||||||||||
| cognitiveburdenanddecreasesannotationquality. | |||||||||||||
| 3SCIFACTiscomparableinsizetorecentscientificdatasets has1,000 questions), and informationextraction(e.g. Sci- | |||||||||||||
| for tasks such as QA (e.g. PubMedQA (Jin et al., 2019) ERC(Luanetal.,2018)has500annotatedabstracts). | |||||||||||||
| 7536 |
cationmodelperformspoorly,suggestingthatthe Humans
| Mice | negationprocessdidnotintroducesevereartifacts. | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Animals | |||||||||||
| Female | |||||||||||
| Male | 3.3 Claimverification | ||||||||||
| --- | ---- | --- | --- | --- | --------------------- | --- | --- | --- | --- | --- | --- |
| Middle Aged | |||||||||||
| Aged | |||||||||||
| Adult | AnnotationForeachclaim,alloftheclaim’scited | ||||||||||
| --- | ----- | --- | --- | --- | ------------------------------------------- | --- | --- | --- | --- | --- | --- |
| Cell Line | |||||||||||
| Signal Transduction abstracts are annotated for evidence. Annotators | |||||||||||
| RNA | |||||||||||
| Receptors | areshownasingleclaim-citedabstractpair,and | ||||||||||
| --- | --------- | --- | --- | --- | ------------------------------------------ | --- | --- | --- | --- | --- | --- |
| Gene Expression Regulation | |||||||||||
| askedtolabelthepairas | SUPPORTS, | REFUTES,or | |||||||||
| --- | --- | --- | --- | --- | --------------------- | --- | --- | --------- | --- | ---------- | --- |
| Models | |||||||||||
| Risk Factors | |||||||||||
| NOINFO. | Althoughourtaskdefinitionallowsfora | ||||||||||
| --- | --- | --- | --- | --- | ------- | ----------------------------------- | --- | --- | --- | --- | --- |
| Cells | |||||||||||
| Cell Differentiation | |||||||||||
| Mutation | singleclaimtobebothsupportedandrefuted(by | ||||||||||
| --- | ---------- | --- | --- | --- | ------------------------------------------- | --- | --- | --- | --- | --- | --- |
| Microscopy | differentabstracts)–anoccurrenceweobserveon | ||||||||||
| Infant | |||||||||||
| real-world | COVID-19 | claims | (§6.3) | – | this never | ||||||
| --- | --- | --- | ------- | ------- | ---------- | -------- | --- | ------ | ------ | --- | ---------- |
| 0.0 | 0.2 | 0.4 0.6 | 0.8 1.0 | ||||||||
| Fraction of evidence abstracts | |||||||||||
| occursinourdataset. | Eachclaimhasasinglelabel. | ||||||||||
| --------- | --------------- | --------- | ------- | ------- | ------------------------------------ | --- | --- | ------------------------- | --- | --- | ----- |
| CountsforeachlabelareshowninTable2a. | Over- | ||||||||||
| Figure 3: | Most frequently | occurring | Medical | Subject | |||||||
| all,theannotatorsfoundevidencein63%ofcited | |||||||||||
| Headings(MeSH)terms(y-axis)amongcitedabstracts. | |||||||||||
| MeSHisacontrolledvocabularyusedforindexingar- abstracts. Iftheannotatorassignsa SUPPORTS or | |||||||||||
| ticles in PubMed. Topics range from clinical trial re- REFUTES label, they must also identify all ratio- | |||||||||||
| ports(“Humans”,“RiskFactors”)tomolecularbiology nalesasdefinedin§2. Table2bprovidesstatistics | |||||||||||
| (“CellLine”,“RNA”). onthenumberofsentencesperrationale,thenum- | |||||||||||
| berofrationalesperclaim/abstractpair,andthe | |||||||||||
| discusssimilartopicstotheevidencedocuments, number of evidence abstracts per claim. No ab- | |||||||||||
| stracthasmorethan3rationalesforagivenclaim, | |||||||||||
| increasing | the difficulty | of abstract | retrieval | and | |||||||
| ---------- | -------------- | ----------- | --------- | --- | --- | --- | --- | --- | --- | --- | --- |
| andallrationalesconsistofatmostthreesentences. | |||||||||||
| makingourmetricsmoreaccuratelyreflectthesys- | |||||||||||
| RationalesinSCIFACTaremutuallyexclusive. | 28 | ||||||||||
| --- | --- | --- | --- | --- | ---------------------------------------- | --- | --- | --- | --- | --- | --- |
| tem’sperformanceonalargeresearchcorpus. | |||||||||||
| rationalescontainnon-contiguoussentences. | |||||||||||
| 3.2 Claimwriting The verifiers included three NLP experts, five | |||||||||||
| lifescienceundergraduates,andfivegraduatestu- | |||||||||||
| AnnotationAnnotatorsareshownasourcecitance | |||||||||||
| dents studying | life sciences. | Annotators | verified | ||||||||
| --- | --- | --- | --- | --- | -------------- | --- | -------------- | --- | ---------- | --- | -------- |
| inthecontextofanarticle,andareaskedtowriteup | |||||||||||
| claimsthattheydidnotwritethemselves. | Annota- | ||||||||||
| --- | --- | --- | --- | --- | ------------------------------------ | --- | --- | --- | --- | --- | ------- |
| tothreeclaimsbasedonthecontentofthecitance; | |||||||||||
| tionguidelinesareprovidedinAppendixD. | |||||||||||
| seeAppendixC.2foranexample. | Thisresultsin | ||||||||||
| --------------------------- | --- | --- | ------------- | --- | ------- | ------ | --- | ------------ | --- | ------- | --------- |
| SCIFACT | claims | are verified | against | abstracts | |||||||
| naturalclaimsbecausetheannotatordoesnotsee | |||||||||||
| rather than | full | articles | since | (1) | abstracts | can be | |||||
| --- | --- | --- | --- | --- | ----------- | ---- | -------- | ----- | --- | --------- | ------ |
| thecitedarticle’sabstract–thecitedabstract–at | |||||||||||
| annotatedmorescalably,(2)evidenceisfoundin | |||||||||||
| the time | of claim writing. | Annotators | are | asked | |||||||
| -------- | ----------------- | ---------- | --- | ----- | --- | --- | --- | --- | --- | --- | --- |
| theabstractinmorethan60%ofcases,and(3)pre- | |||||||||||
| toskipcitancesthatdonotmakestatementsabout | |||||||||||
| viousattemptsatfull-documentannotationsuffered | |||||||||||
| specificscientificfindings. | |||||||||||
| fromlowannotatoragreement(§7). | |||||||||||
| The claim | writers included | four experts | with | ||||||||
| --------- | ---------------- | --- | ------------ | ---- | --- | --- | --- | --- | --- | --- | --- |
| backgroundinscientificNLP,fifteenundergradu- QualityWeassign232claim-abstractpairsforin- | |||||||||||
| dependent | re-annotation. | The | label | agreement | is | ||||||
| ------------- | ------------------ | --- | -------- | -------- | --------- | -------------- | --- | --- | ----- | --------- | --- |
| ates studying | the life sciences, | and four | graduate | ||||||||
| 0.75Cohen’sκ,comparablewiththe0.68Fleiss’ | |||||||||||
| students(doctoralormedical)inthelifesciences. | |||||||||||
| Detailedinformationontheannotatortrainingpro- κ reported in Thorne et al. (2018), and 0.70 Co- | |||||||||||
| hen’sκreportedinHanselowskietal.(2019). | |||||||||||
| cess can | be found in Appendix | C.3. The | claim- | To | |||||||
| -------- | -------------------- | --- | -------- | ------ | ------- | --------- | ---------- | --- | --- | ----- | --------- |
| measure | rationale | agreement, | we | treat | each sen- | ||||||
| writinginterfaceisshowninAppendixD. | |||||||||||
| tenceaseitherclassifiedas“partofarationale”or | |||||||||||
| Claim negation | Unless | the authors | of the | source | |||||||
| -------------- | ------ | ----------- | ------ | ------ | --- | --- | --- | --- | --- | --- | --- |
| “notpartofarationale”andcomputesentence-level | |||||||||||
| citance | were mistaken, | cited | articles should | pro- | |||||||
| --------------- | ------------------ | ----- | --------------- | -------- | ---------- | --------------------------- | --- | ---- | --- | --- | --- |
| agreement. | TheresultingCohen’sκis0.71. | ||||||||||
| vide supporting | evidence | for | the claims | made in | |||||||
| a citance. | To obtain examples | where an | abstract | ||||||||
| 4 The | SCIFACT | task | |||||||||
| REFUTESaclaim,anNLPexpertwrotenegations | |||||||||||
| of existing claims, taking precautions not to bias TaskFormulationTheinputstoourtaskareasci- | |||||||||||
| thenegationsbyusingobviouskeywordslike“not” entificclaimcandacorpusofabstractsA. Allab- | |||||||||||
| (Schusteretal.,2019;Gururanganetal.,2018). In stractsa ∈ Aarelabeledasy(c,a) ∈ {SUPPORTS, | |||||||||||
| §6.1, we demonstrate that a “claim-only” verifi- REFUTES, NOINFO } with respect to a claim c. | |||||||||||
| 7537 |
Fold SUPPORTS NOINFO REFUTES All goldevidenceabstractforc,and(2)Thepredicted label is correct: y(c,a) = y(c,a). It is correctly Train 332 304 173 809 (cid:98) Dev 124 112 64 300 rationalized if,inaddition,thepredictedrationale Test 100 100 100 300 sentencescontainagoldrationale,i.e.,thereexists All 556 516 337 1409 somegoldrationaleR i (c,a) ⊆ S(cid:98)(c,a). (a)DistributionofclaimlabelsinSCIFACT. LikeFEVER,whichlimitsthemaximumnumber ofpredictedrationalesentencestofive, SCIFACT 0 1 2 3+ limitstothreepredictedrationalesentences. Over- Citedabstractsperclaim - 1278 86 45 all performance is measured by the micro-F1 of Evidenceabstractsperclaim 516 830 37 26 theprecisionandrecalloverthecorrectly-labeled Rationalesperabstract - 552 290 153 Sentencesperrationale - 1542 92 11 andcorrectly-rationalizedevidenceabstracts. We refertotheseevaluationsasAbstract and (b)Evidencecountsatvariouslevelsofgranularity.Forexam- Label-Only ple,Column2oftherow“Rationales/abstract”indicatesthat Abstract Label+Rationale ,respectively. 290claim/abstractpairsaresupportedby2distinctrationales. Sentence-levelevaluationmeasuresperformance Table 2: Statistics on claim labels, and the number of inidentifyingindividualrationalesentences. Un- evidenceabstractsandrationalesperclaim. like the abstract-level metrics, this evaluation pe- nalizesthepredictionofextrarationalesentences. A predicted rationale sentence s(c,a) is cor- (cid:98) The abstracts that either SUPPORT or REFUTE c rectlyselected if(1)Itisamemberofsomegold arereferredtoasevidenceabstractsforc,denoted rationaleR (c,a),(2)allothersentencesfromthe i as E(c). Each evidence abstract a ∈ E(c) is an- same gold rationale R (c,a) are among the pre- i notated with rationales. A single rationale R i is dicted S(cid:98)(c,a), and (3) y (cid:98) (c,a) (cid:54)= NOINFO 4. It is acollectionofsentences{r (c,a),...,r (c,a)}, 1 m correctly labeled if, in addition, the abstract a is wheremisthenumberofsentencesinrationaleR . i correctlylabeled: y(c,a) = y(c,a). (cid:98) We denote the set of all rationales as R(c,a) = Overallperformanceismeasuredbythemicro- {R (c,a),...,R (c,a)}. 1 n F1oftheprecisionandrecallofcorrectly-selected Given a claim c and a corpus A, the system andcorrectly-labeledrationalesentences,denoted mustpredictasetofevidenceabstractsE(cid:98)(c). For Sentence and Sentence . Selection-Only Selection+Label each abstract a ∈ E(cid:98)(c), it must predict a label Forsentence-levelevaluation,wedonotlimitthe y (cid:98) (c,a), and a collection of rationale sentences numberofpredictedrationalesentences,sincethe S(cid:98)(c,a) = {s (cid:98)1 (c,a),...,s (cid:98)(cid:96) (c,a)}. Note that al- evaluationpenalizesmodelsthatover-predict. thoughthegoldannotationsmaycontainmultiple separaterationales,tosimplifythepredictiontask 5 VERISCI: Baselinemodel weonlyrequirethemodeltopredictasinglecol- Wedevelopabaseline(referredtoasVERISCI)that lectionofrationalesentences;thesesentencesmay takes a claim c and corpus A as input, identifies encompassmultiplegoldrationales. evidenceabstractsE(cid:98)(c),andpredictsalabely (cid:98) (c,a) TaskEvaluationWeevaluatethetaskattwolevels andrationalesentencesS(cid:98)(c,a)foreacha ∈ E(cid:98)(c). of granularity. For abstract-level evaluation, we Followingthe“BERT-to-BERT”modelpresented assessthemodel’sabilitytoidentifytheabstracts inDeYoungetal.(2020a);Soleimanietal.(2019), thatsupportorrefutetheclaim. Forsentence-level VERISCIisapipelineofthreecomponents: evaluation, we evaluate the model’s performance
- ABSTRACTRETRIEVAL retrieves k abstracts atidentifyingthesentencessufficienttojustifythe withhighestTF-IDFsimilaritytotheclaim. abstract-levelpredictions. Weconductevaluations
- RATIONALESELECTION identifies rationale in both the “Open” FEVER-style (Thorne et al., sentencesS(cid:98)(c,a)foreachabstract.
- setting where the evidence abstracts must
- LABELPREDICTIONmakesthefinallabelpre- beretrieved,andthe“Oracleabstract” ERASER- dictiony(c,a). (cid:98) style (DeYoung et al., 2020a) setting where the Rationale selection Given a claim c and ab- goldevidenceabstractsE(c)areprovided. stract a, we train a model to predict z (cid:44) i Abstract-level evaluation is inspired by the 4Condition(3)eliminatesrationalesentenceswhichwere FEVERscore. Givenaclaimc,apredictedevidence identifiedbytherationaleselector,butprovedinsufficientto abstracta ∈ E(cid:98)(c)iscorrectlylabeled if(1)aisa justifyafinalSUPPORTS/REFUTESdecision 7538
1[a isarationalesentence] for each sentence a RATIONAL-SELECT. LABEL-PRED.
| i | i | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| in a. For each sentence, we encode the concate- Trainingdata P R F1 ACC. | ||||||||||||
| nated sequence | w = | [a ,SEP,c] | using | a BERT- | ||||||||
| ------------------- | ---------- | ----- | --------------------- | -------- | ------- | ------- | ------------- | --- | ---- | --------- | --- | ---- |
| i | i | |||||||||||
| FEVER | 41.5 | 57.9 48.4 | 67.6 | |||||||||
| style language | model | and | predict | a score | z˜ = | |||||||
| i | UKPSnopes | 42.5 | 62.3 50.5 | 71.3 | ||||||||
| SCIFACT | 73.7 | 70.5 72.1 | 75.7 | |||||||||
| σ[f(CLS(w | ))],whereσ | isthesigmoidfunction, | ||||||||||
| i | FEVER+SCIFACT | 72.4 | 67.2 69.7 | 81.9 | ||||||||
| f isalinearlayerand | CLS(w | i )isthe | CLS | token | ||||||||
| from the encoding of w . We train the model on Sentenceencoder P R F1 ACC. | ||||||||||||
| i | ||||||||||||
| pairs of claims and their cited abstracts and min- SCIBERT 74.5 74.3 74.4 69.2 | ||||||||||||
| BioMedRoBERTa | 75.3 | 69.9 72.5 | 71.7 | |||||||||
| ------------------- | --- | --- | ------------ | --- | ------- | --------- | ------------- | --- | ---- | --------- | --- | ---- |
| imize cross-entropy | loss between | z i and | z˜. i For | |||||||||
| RoBERTa-base | 76.1 | 66.1 70.8 | 62.9 | |||||||||
| eachclaim,weusecitedabstractslabeledNOINFO, | ||||||||||||
| RoBERTa-large | 73.7 | 70.5 72.1 | 75.7 | |||||||||
| ------- | ---------------- | --- | --------- | --- | ----------- | --------- | ------------- | --- | ---- | --------- | --- | ---- |
| as well | as non-rationale | sentences | from | abstracts | ||||||||
| Modelinputs | P | R F1 | ACC. | |||||||||
| labeled | SUPPORTS | and | REFUTES | as negative | ex- | |||||||
| Claim-only | - | - - | 44.5 | |||||||||
| amples. To make predictions, we select all sen- Abstract-only 60.1 60.9 60.5 53.3 | ||||||||||||
| tencesa | withz˜ | > tasrationalesentences,where | ||||||||||
| ------- | ------ | ----------------------------- | --- | --- | --- | --- | ---------------------------------------------- | --- | --- | --- | --- | --- |
| i | i | |||||||||||
| Table3: Comparisonofdifferenttrainingdatasets, | en- | |||||||||||
| t ∈ [0,1]istunedonthedevset(AppendixA.1). | ||||||||||||
| coders, and | model | inputs for | RATIONALESELECTION | |||||||||
| ---------------- | -------- | --------- | ------ | ------------- | --- | ---------- | -------------------- | ----- | ---------- | ------------------ | ------ | ------- |
| Label prediction | Sentences | identified | by the ra- | |||||||||
| and LABELPREDICTION, | evaluated | on the | SCIFACT | |||||||||
| tionale | selector | are | passed | to a separate | BERT- | |||||||
| devset. Theclaim-onlymodelcannotselectrationales. | ||||||||||||
| based model | to | make | the | final labeling | decision. | |||||||
| ----------- | ----- | ----- | -------- | -------------- | -------------- | --------- | --- | --- | --- | --- | --- | --- |
| Given a | claim | c and | abstract | a, | we concatenate | |||||||
| the claim and the predicted rationale sentences VERISCI, and (4) discuss some modeling chal- | ||||||||||||
| lengespresentedbythedataset. | ||||||||||||
| u = [s | (c,a),...s | (c,a),SEP,c]5, | and | predict | ||||||||
| --------- | ------------- | ---------------- | -------------- | --- | ------ | ------- | --- | --- | --- | --- | --- | --- |
| (cid:98)1 | (cid:98)(cid:96) | |||||||||||
| y˜(c,a) = | φ[f(CLS(u))], | where | φ | is the | softmax | |||||||
| 6.1 Pipelinecomponents | ||||||||||||
| function,andf | isalinearlayerwiththreeoutputs | |||||||||||
| ------------- | --- | ------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| representingthe{SUPPORTS, REFUTES, NOINFO We examine the effects of different training | ||||||||||||
| datasets,sentenceencoders,andmodelinputson | ||||||||||||
| } labels. | We minimize | the | cross-entropy | loss be- | ||||||||
| --------- | ----------- | --- | --- | ------------- | --- | -------- | --- | --- | --- | --- | --- | --- |
| tweeny˜(c,a)andthetruelabely(c,a). the performance of the RATIONALESELECTION | ||||||||||||
| Wetrainthemodelonpairsofclaimsandtheir and LABELPREDICTION modules. The RATIO- | ||||||||||||
| citedabstractsusinggoldrationalesasinput. For NALESELECTIONmoduleisevaluatedonitsability | ||||||||||||
| toselectrationalesentencesgivengoldabstracts6. | ||||||||||||
| cited abstracts | labeled | NOINFO, | we choose | the | ||||||||
| --------------- | --- | ------- | ------- | --- | --------- | --- | --- | --- | --- | --- | --- | --- |
| k sentences from the cited abstract with high- TheLABELPREDICTIONmoduleisevaluatedonits | ||||||||||||
| est TF-IDF similarity to the claim as input ra- 3-waylabelclassificationaccuracygivengoldratio- | ||||||||||||
| nalesfromcitedabstracts. | Citedabstractslabeled | |||||||||||
| --------- | --- | ----------- | --- | ------ | --- | --------- | ------------------------ | --- | --- | --------------------- | --- | --- |
| tionales. | For | prediction, | we use | the | predicted | |||||||
| rationale sentences S(cid:98)(c,a) as input and predict NOINFOareincludedintheevaluation. Theseab- | ||||||||||||
| yˆ(c,a) = argmaxy˜(c,a). NOINFO is predicted stractshavenogoldrationalesentences;asin§5, | ||||||||||||
| weprovidethek | ||||||||||||
| forabstractswithnorationalesentences. mostsimilarsentencesfromthe | ||||||||||||
| abstractasinput(moredetailsinAppendixA). | ||||||||||||
| Weexperimentedwithalabelpredictionmodel | ||||||||||||
| whichencodesentireabstractsviatheLongformer | ||||||||||||
| Training Data | We | train | on (1) FEVER, | (2) UKP | ||||||||
| --- | --- | --- | --- | --- | --- | --- | ------------- | --- | ----- | ------------- | --- | ------- |
| (Beltagy et al., 2020), and makes predictions us- Snopes,(3)SCIFACT,and(4)FEVERpretraining | ||||||||||||
| ingthedocument-level CLS token. Performance followedby SCIFACTfine-tuning. RoBERTa-large | ||||||||||||
| wasnotcompetitivewithourpipelinesetup,likely | ||||||||||||
| (Liuetal.,2019)isusedasthesentenceencoder. | ||||||||||||
| because | the label | predictor | struggles | to | identify | |||||||
| ------- | --------- | --------- | --- | --------- | --- | -------- | ---------------- | --- | ------------ | ------- | --- | ------ |
| Sentence encoder | We fine-tune | SCIBERT | (Belt- | |||||||||
| relevantinformationwhengivenfullabstracts. | ||||||||||||
| agyetal.,2019),BioMedRoBERTa(Gururangan | ||||||||||||
| etal.,2020),RoBERTa-base,andRoBERTa-large. | ||||||||||||
| 6 Experiments | ||||||||||||
| SCIFACTisusedastrainingdata. | ||||||||||||
| Inourexperiments,we(1)analyzetheperformance | ||||||||||||
| Model Inputs | We | examine | the | performance | of | |||||||
| --- | --- | --- | --- | --- | --- | --- | ------------ | --- | ------- | --- | ----------- | --- |
| ofeachindividualcomponentofVERISCI,(2)eval- “claim-only” and “abstract-only” models trained | ||||||||||||
| uatefulltaskperformanceinboththe“Oracleab- | ||||||||||||
| onSCIFACT,usingRoBERTa-largeasthesentence | ||||||||||||
| stract”and“Open”settings,(3)presentpromising | ||||||||||||
| encoder. Theclaim-onlymodelmakeslabelpredic- | ||||||||||||
| results verifying | claims | about | COVID-19 | using | ||||||||
| ----------------- | --- | ------ | ----- | -------- | --- | ----- | ------------------ | --- | ------------------ | --- | --- | ------ |
| 6Our FEVER-trained | RATIONALESELECTION | module | ||||||||||
| 5WetruncatetherationaleinputifitexceedstheBERT achieves79.9sentence-levelF1ontheFEVERtestset,virtu- | ||||||||||||
| tokenlimit.cisnevertruncated. allyidenticalto79.6reportedinDeYoungetal.(2020a). | ||||||||||||
| 7539 |
| Sentence-level | Abstract-level | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selection-Only | Selection+Label | Label-Only | Label+Rationale | |||||||||
| Retrieval | Model | P | R | F1 | P | R F1 | P R | F1 | P | R | F1 | |
| Oraclerationale 1 100.0 80.5 89.2 89.6 72.2 79.9 90.1 77.5 83.3 90.1 77.5 83.3 | ||||||||||||
| 2.1 | 3.0 | 2.4 | 2.4 | |||||||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Oracle | ||||||||||||
| 43.8 | 37.2 | 66.3 | 51.8 | |||||||||
| --- | --- | --- | --- | ---- | --- | ---- | --- | --- | ---- | --- | --- | ---- |
| abstract Zero-shot 2 42.5 45.1 2.0 36.1 38.4 2.3 86.9 53.6 3.1 67.9 41.9 3.4 | ||||||||||||
| VERISCI 3 76.1 63.8 69.4 66.5 55.7 60.6 87.3 65.3 74.7 84.9 63.5 72.7 | ||||||||||||
| 2.6 | 3.1 | 2.8 | 2.9 | |||||||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Oraclerationale 4 100.0 56.5 72.2 3.3 87.6 49.5 63.2 3.7 88.9 54.1 67.2 3.2 88.9 54.1 67.2 3.2 | ||||||||||||
| Open Zero-shot 5 28.7 37.6 32.5 2.3 23.7 31.1 26.9 2.3 56.0 42.3 48.2 3.3 42.3 32.0 36.4 3.3 | ||||||||||||
| VERISCI 6 45.0 47.3 46.1 3.0 38.6 40.5 39.5 3.0 47.5 47.3 47.4 3.1 46.6 46.4 46.5 3.1 | ||||||||||||
| Table 4: Test set performance on SCIFACT, according to the metrics from §4. For the “Oracle abstract” rows, | ||||||||||||
| thesystemisprovidedwithgoldevidenceabstracts. “Oraclerationale”rowsindicatethatthegoldrationalesare | ||||||||||||
| providedasinput. “Zero-shot”indicateszero-shotperformanceofaverificationsystemtrainedonFEVER. Addi- | ||||||||||||
| tionally,standarddeviationsarereportedassubscriptsforallF1scores. SeeAppendixBforstandarddeviations | ||||||||||||
| onallreportedmetrics. | ||||||||||||
| tionsbasedontheclaimtextalone,withoutaccess inordertoidentifyrelevantevidence. | ||||||||||||
| to evidence | abstracts. | The abstract-only | model | |||||||||
| --------------------------------------------- | ---------- | ----------------- | --- | ----- | --- | -------- | --- | --- | --- | --- | --- | --- |
| selectsrationalesentencesandmakeslabelpredic- | 6.2 | Fulltask | ||||||||||
| tionswithoutaccesstotheclaim. | ||||||||||||
| Experimental | setup | Based | on the | results | from | |||||||
| --- | --- | --- | --- | --- | ------------ | --- | ----- | ----- | --- | ------ | ------- | ---- |
| ResultsTheresultsareshowninTable3. For LA- §6.1,weusetheRATIONALESELECTIONmodule | ||||||||||||
| trainedon | SCIFACT | only,andthe | LABELPREDIC- | |||||||||
| --- | --- | --- | --- | --- | --------- | --- | ------- | ----------- | --- | ------------ | --- | --- |
| BELPREDICTION,thebestperformanceisachieved | ||||||||||||
| by training first on the large FEVER dataset and TIONmoduletrainedonFEVER+SCIFACTforour | ||||||||||||
| thenfine-tuningonthesmallerin-domainSCIFACT finalend-to-endsystem VERISCI. AlthoughSCIB- | ||||||||||||
| trainingset. TounderstandthebenefitsofFEVER ERTperformsslightlybetteronrationaleselection, | ||||||||||||
| pretraining,weexaminedtheclaim/evidencepairs usingRoBERTa-largeforbothRATIONALESELEC- | ||||||||||||
| wherethe FEVER+ SCIFACT-trainedmodelmade TION and LABELPREDICTION gavethebestfull- | ||||||||||||
| correctpredictionsbuttheSCIFACT-trainedmodel pipeline performance on the dev set, so we use | ||||||||||||
| RoBERTa-largeforbothcomponents. | FortheAB- | |||||||||||
| -------- | ------------- | ------------ | --- | -------- | ------------------------------- | --- | --- | --- | --- | --- | --------- | --- |
| did not. | In 36 / 44 of | these cases, | the | SCIFACT- | ||||||||
| trainedmodelpredictsNOINFO. Thuspretraining STRACTRETRIEVALmodule,thebestdevsetfull- | ||||||||||||
| on FEVER appears to improve the model’s abil- pipeline performance was achieved by retrieving | ||||||||||||
| thetopk | = 3documents. | |||||||||||
| ---------------- | ------- | ---------- | ------------- | --- | ------- | --- | ------------- | --- | --- | --- | --- | --- |
| ity to recognize | textual | entailment | relationships | |||||||||
| betweenevidenceandclaim–particularlyrelation- | ||||||||||||
| Model | comparisons | We | report | performance | of | |||||||
| --------------- | ---------------------- | --- | --- | ---- | ----- | ----------- | --------- | --- | ------- | ----------- | ---------- | --- |
| ships indicated | by non-domain-specific | cues | like | |||||||||
| three | model | variants. | For the | “Oracle | rationale” | |||||||
| “isassociatedwith”or“hasanimportantrolein”. setting,the RATIONALESELECTION moduleisre- | ||||||||||||
| For RATIONALESELECTION, training on SCI- placedbyanoraclewhichoutputsgoldrationales | ||||||||||||
| FACT alone produces the best results. We exam- forcorrectlyretrieveddocuments,andnorationales | ||||||||||||
| inedtherationalesthattheSCIFACT-trainedmodel forincorrectretrievals. The“Zero-shot”settingre- | ||||||||||||
| portsthezero-shotgeneralizationperformanceof | ||||||||||||
| identified | but the FEVER- | trained | model | missed, | ||||||||
| ---------- | -------------- | ------- | ----- | ------- | --- | --- | --- | --- | --- | --- | --- | --- |
| and found that they generally contain science- a model trained on FEVER (the results on UKP | ||||||||||||
| specific vocabulary. Thus, training on additional Snopeswereslightlyworse). VERISCIreportsthe | ||||||||||||
| performanceofourbestsystem. | ||||||||||||
| out-of-domaindataprovideslittlebenefit. | ||||||||||||
| RoBERTa-large exhibits the strongest perfor- Results The results are shown in Table 4. In the | ||||||||||||
| mance on label prediction, while SCIBERT has oracleabstractsetting,theabstract-levelF1scores | ||||||||||||
| areroughlycomparabletolabelclassificationaccu- | ||||||||||||
| a slight | edge on rationale | selection. | The | “claim- | ||||||||
| -------- | ----------------- | ---------- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | --- |
| only”modelexhibitsverypoorperformance,which racies,andtheAbstract scoreinRow | ||||||||||||
| Label+Rationale | ||||||||||||
| providessomereassurancethattheclaimnegation 3impliesanend-to-endclassificationaccuracyof | ||||||||||||
| roughly70%,givengoldabstracts. | ||||||||||||
| proceduredescribedin§3.2doesnotintroduceob- | ||||||||||||
| viousstatisticalartifacts. Similarly,thepoorperfor- Access to in-domain data during training | ||||||||||||
| manceofthe“abstract-only”modelindicatesthat clearly improves performance. Despite the | ||||||||||||
| themodelneedsaccesstotheclaimbeingverified small size of SCIFACT, training on these data | ||||||||||||
| 7540 |
Reasoningtype Example Claim: Rapamycinslowsaginginfruitflies. Science Evidence: ...feedingrapamycintoadultDrosophilaproduceslifespanextension... background GoldVerdict: SUPPORTS Reasoning: Drosophilaisatypeoffruitfly. Claim: Inhibitingglucose-6-phospatedehydrogenaseimpairslipogenesis Evidence: ...suppressionof6PGDincreasedlipogenesis Directionality GoldVerdict: REFUTES Reasoning: Adecrease(notincrease)inlipogenesiswouldindicatelipogenesisimpairment. Claim: Bariatricsurgeryimprovesresolutionofdiabetes. Evidence: StrongassociationswerefoundbetweenbariatricsurgeryandtheresolutionofT2DM, Numerical withaHRof9.29(95%CI6.84-12.62)... reasoning GoldVerdict: SUPPORTS Reasoning: AHR(hazardratio)thatisgreaterthan1with95%confidenceindicatesimprovement. Claim: Majorvaultprotein(MVP)functionstodecreasetumoraggression. Evidence: KnockoutofMVPleadstomiR-193aaccumulation...inhibitingtumorprogression Causeand effect GoldVerdict: REFUTES Reasoning: Knockingout(removing)MVPinhibitstumorprogression→MVPincreasestumor aggression. Claim: Lowsaturatedfatdietshaveadverseeffectsonthedevelopmentofinfants Evidence: Neurologicaldevelopmentofchildrenintheinterventiongroupwasatleastasgoodas... Coreference thecontrolgroup GoldVerdict: REFUTES Reasoning: Theinterventiongroupinthisstudywasplacedonalowsaturatedfatdiet. Table 5: Reasoning types required to verify SCIFACT claims which are classified incorrectly by our modeling baseline. Wordscrucialforcorrectverificationarehighlighted. leads to relative improvements of 47% on sultsindicatethattheobserveddifferencesinmodel open Sentence , and 28% on open performancearestatisticallyrobustandcannotbe Selection+Label Abstract Label+Rationale overFEVERalone(Row6vs. attributedtorandomvariationinthedataset. Row5). Thethreepipelinecomponentsmakesimi- 6.3 VerifyingclaimsaboutCOVID-19 larcontributionstotheoverallmodelerror. Replac- ingRATIONALESELECTIONwithanoracleleads Weconductexploratoryexperimentsusingoursys- toaroughly20-pointriseinSentence Selection+Label tem to verify claims concerning COVID-19. We F1(Row6vs. Row4). ReplacingABSTRACTRE- taskedamedicalstudenttowrite36COVID-related TRIEVALwithanoracleaswellleadstoagainof claims. For each claim c, we used VERISCI to roughly20morepoints(Row4vs. Row1). predictevidenceabstractsE(cid:98)(c). Theannotatorex- Nearly all correctly-labeled abstracts are sup- amined each (c,E(cid:98)(c)) pair. A pair was labeled portedbyatleastonerationale. Thereisonlyatwo- plausibleifE(cid:98)(c)wasnonempty,andatleasthalfof point difference in F1 between Abstract Label-Only theevidenceabstractsinE(cid:98)(c)werejudgedtohave and Abstract in the oracle setting reasonablerationalesandlabels. For23/36claims, Label+Rationale (Row 3), and a one-point difference in the theresponseofVERISCIwasdeemedplausibleby open setting (Row 6). The differences between ourannotator,demonstratingthatVERISCIisable Sentence andSentence are to successfully retrieve and classify evidence in Selection-Only Selection+Label larger,causedbyexampleswherethemodelfinds manycases. TwoexamplesareshowninTable1. theevidencebutfailstopredictitsrelationshipto Inbothcases,oursystemidentifiesbothsupporting theclaim. Weexaminethesein§6.4. and refutingevidence. Weevaluatethestatisticalrobustnessofourre- 6.4 Erroranalysis sultsbygenerating10,000bootstrap-resampledver- sions of the test set (Dror et al., 2018) and com- TobetterunderstandtheerrorsmadebyVERISCI, puting the standard deviation of all performance weconductamanualanalysisoftestsetpredictions metrics. Table4showsthestandarddeviationsin whereanevidenceabstractwascorrectlyretrieved, F1score. Uncertaintiesonallmetricsforboththe butwherethemodelfailedtoidentifyanyrelevant devandtestsetcanbefoundinAppendixB.There- rationalesorpredictedanincorrectlabel. Weiden- 7541
tifyfivemodelingcapabilitiesrequiredtocorrect knowledgeintensivetaskswhichrequireanunder- thesemistakes(Table5providesexamples): standingoftherelationshipbetweenaninputquery andrelevantsupportingtext.
| Science | background | includes | knowledge | of | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| domain-specificlexicalrelationships. Automated evidence synthesis (Marshall and | ||||||||||||
| Directionalityrequiresunderstandingincreasesor Wallace, 2019; Beller et al., 2018; Tsafnat et al., | ||||||||||||
| decreasesinscientificquantities. 2014;Marshalletal.,2017)seekstoautomatethe | ||||||||||||
| Numericalreasoninginvolvesinterpretingnumer- processofcreatingsystematicreviewsofthemed- | ||||||||||||
| icalorstatisticalfindings. icalliterature7 –forinstance,byextractingPICO | ||||||||||||
| Cause and effect requires reasoning about coun- snippets (Nye et al., 2018) and inferring the out- | ||||||||||||
| comesofclinicaltrials(Lehmanetal.,2019;DeY- | ||||||||||||
| terfactuals. | ||||||||||||
| Coreferenceinvolvesdrawingconclusionsusing oungetal.,2020b). Wehopethatsystemsforclaim | ||||||||||||
| contextstatedoutsideofarationalesentence. verificationwillserveascomponentsinfutureevi- | ||||||||||||
| dencesynthesisframeworks. | ||||||||||||
| 7 Relatedwork | ||||||||||||
| 8 Conclusionandfuturework | ||||||||||||
| Fact checking | and | rationalized | NLP | models | ||||||||
| ------------- | ------- | -------- | ------------ | -------- | ---------- | ------- | ---------------------------------------- | ---- | ------- | --------------- | -------- | ----------- |
| Claim verification | allows | us | to trace | the sources | ||||||||
| Fact-checking | datasets | include | PolitiFact | (Vla- | ||||||||
| andmeasuretheveracityofscientificclaims. | These | |||||||||||
| chos and | Riedel, | 2014), | Emergent | (Ferreira | and | |||||||
| abilities | have | emerged | as particularly | important | ||||||||
| Vlachos, | 2016), | LIAR | (Wang, | 2017), | SemEval | |||||||
| 2017Task8RumorEval(Derczynskietal.,2017), in the context of the current pandemic, and the | ||||||||||||
| broader | reproducibility | crisis | in science. | In this | ||||||||
| ---------------------- | ------ | ------- | ------ | ----------- | ------ | ------ | -------- | --------------- | --- | -------- | ------------- | ------- |
| Snopes | (Popat | et al., | 2017), | CLEF-2018 | Check- | |||||||
| article, | we formalize | the task | of scientific | claim | ||||||||
| That! (Barro´n-Ceden˜o | et | al., 2018), | Verify | (Baly | ||||||||
| et al., 2018), Perspectrum (Chen et al., 2019), verification,andreleaseadataset(SCIFACT)and | ||||||||||||
| models | (VERISCI) | to support | work | on this task. | ||||||||
| ----- | ------- | --- | ----------- | --- | ------- | ------ | ------ | --------- | --- | ---------- | ---- | ------------- |
| FEVER | (Thorne | et | al., 2018), | and UKP | Snopes | |||||||
| Ourresultsindicatethatitispossibletotrainmod- | ||||||||||||
| (Hanselowski | et al., | 2019). | Hanselowski | et al. | ||||||||
| ------------------------------ | --- | ------- | ------ | ----------- | ----------- | ------ | ------- | ---------- | ------------- | --- | --- | ----------- |
| els for | scientific | fact-checking | and | deploy them | ||||||||
| (2019)providesathoroughreview. | Toourknowl- | |||||||||||
| edge, thereare noexistingdata setsfor scientific with reasonable efficacy on real-world claims re- | ||||||||||||
| latedtoCOVID-19. | ||||||||||||
| claimverification. | Werefertoourtaskas“claim | |||||||||||
| ------------------ | --- | --- | ------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Scientificclaimverificationpresentsanumber | ||||||||||||
| verification”ratherthan“fact-checking”toempha- | ||||||||||||
| sizethatourfocusistohelpresearchersmakesense ofpromisingavenuesforresearchonmodelscapa- | ||||||||||||
| bleofincorporatingbackgroundinformation,rea- | ||||||||||||
| ofscientificfindings,nottocounterdisinformation. | ||||||||||||
| soning | about | scientific | processes, | and assessing | ||||||||
| --- | --- | --- | --- | --- | --- | --- | ------ | ----- | ---------- | ---------- | --- | ------------- |
| Fact-checkingisoneofanumberoftaskswhere | ||||||||||||
| the strength | and | provenance | of various | evidence | ||||||||
| ------- | ----------- | --- | ---------- | --- | ---------- | ------- | ------------ | --- | ---------- | --- | ---------- | -------- |
| a model | is required | to justify | a | prediction | via ra- | |||||||
| tionalesfromthesourcedocument. TheERASER sources. Thislastchallengewillbeespeciallycru- | ||||||||||||
| cialforfutureworkthatseekstoverifyscientific | ||||||||||||
| dataset | (DeYoung | et al., | 2020a) | provides | a suite | |||||||
| ------- | -------- | --- | ------- | ------ | -------- | ------- | --- | --- | --- | --- | --- | --- |
| claimsagainstsourcesotherthantheresearchlit- | ||||||||||||
| of benchmark | datasets | (including | SCIFACT) | for | ||||||||
| ------------ | --- | -------- | ---------- | --- | -------- | --- | --- | --- | --- | --- | --- | --- |
| evaluatingrationalizedNLPmodels. erature–forinstance,socialmediaandthenews. | ||||||||||||
| We hope | that | the resources | presented | in this pa- | ||||||||
| --- | --- | --- | --- | --- | --- | --- | ------- | ---- | ------------- | --- | --------- | ----------- |
| RelatedscientificNLPtasksThecitationcontex- | ||||||||||||
| perencouragefutureresearchontheseimportant | ||||||||||||
| tualization | task | (Cohan | et | al., 2015; | Jaidka | et al., | ||||||
| ----------- | ---- | ------ | --- | ---------- | ------ | ------- | --- | --- | --- | --- | --- | --- |
| challenges,andhelpfacilitateprogresstowardthe | ||||||||||||
| 2017)istoidentifyspansinaciteddocumentthat | ||||||||||||
| broadergoalofscientificdocumentunderstanding. | ||||||||||||
| arerelevanttoaparticularcitationinacitingdoc- | ||||||||||||
| ument. Unlike SCIFACT, these citations are not Acknowledgments | ||||||||||||
| re-written | into | atomic | claims | and are | therefore | |||||||
| ---------------------- | ---- | ------ | ------------------------ | --- | ------- | --------- | ----------------- | --- | ------------- | --- | ----------------- | -------- |
| This research | was supported | by the | ONR MURI | |||||||||
| moredifficulttoverify. | Expertannotatorsachieved | |||||||||||
| N00014-18-1-2670, | ONR | N00014-18-1-2826, | ||||||||||
| verylow(21.7%)inter-annotatoragreementonthe | ||||||||||||
| DARPAN66001-19-2-4031,NSF(IIS1616112), | ||||||||||||
| BioMedSummdataset(Cohenetal.,2014),which | ||||||||||||
| contains314citationsreferencing20papers. Allen Distinguished Investigator Award, and the | ||||||||||||
| Sloanfellowship. | WethanktheSemanticScholar | |||||||||||
| --- | --- | --- | --- | --- | --- | --- | ---------------- | --- | ------------------------- | --- | --- | --- |
| Biomedicalquestionansweringdatasetsinclude | ||||||||||||
| teamatAI2,UW-NLP,andH2labatUWforhelp- | ||||||||||||
| BioASQ(Tsatsaronisetal.,2015)andPubMedQA | ||||||||||||
| (Jin et al., 2019), which contain 855 and 1,000 fulcommentsandfeedback. | ||||||||||||
| “yes/no”questionsrespectively(Guetal.,2020). | ||||||||||||
| 7https://www.cochranelibrary.com/ | ||||||||||||
| Claimverificationandquestionansweringareboth- about/about-cochrane-reviews | ||||||||||||
| 7542 |
References Jay DeYoung, Eric Lehman, Ben Nye, Iain James
Marshall, and Byron C. Wallace. 2020b. Evi-
Ramy Baly, Mitra Mohtarami, James Glass, Llu´ıs
dence inference 2.0: More data, better models. In
Marquez,AlessandroMoschitti,andPreslavNakov. BioNLP@ACL. 2018. Integratingstancedetectionandfactchecking inaunifiedcorpus. InNAACL. RotemDror,GiliBaumer,SegevShlomov,andRoiRe- ichart.2018. Thehitchhiker’sguidetotestingstatis- Alberto Barro´n-Ceden˜o, Tamer Elsayed, Reem ticalsignificanceinnaturallanguageprocessing. In Suwaileh, Llu´ıs Marquez i Villodre, Pepa ACL.
Atanasova, Wajdi Zaghouani, Spas Kyuchukov,
Giovanni Da San Martino, and Preslav Nakov. Bradley Efron and Robert Tibshirani. 1993. An intro-
2018. Overviewoftheclef-2018checkthat! labon ductiontothebootstrap.
automaticidentificationandverificationofpolitical
claims.task2: Factuality. InCLEF. William Ferreira and Andreas Vlachos. 2016. Emer-
gent: a novel data-set for stance classification. In
Elaine Beller, Justin Clark, Guy Tsafnat, Clive Elliott NAACL.
Adams, Heinz Diehl, Hans Lund, Mourad Ouzzani,
Kristina Thayer, James Thomas, Tari Turner, J. S. Yu Gu, Robert Tinn, Hao Cheng, M. Lucas,
Xia,KarenA.Robinson,andPaulPGlasziou.2018. Naoto Usuyama, Xiaodong Liu, Tristan Nau-
Makingprogresswiththeautomationofsystematic mann, Jianfeng Gao, and Hoifung Poon. 2020.
reviews: principles of the international collabora- Domain-specific language model pretraining for
tionfortheautomationofsystematicreviews(icasr). biomedical natural language processing. ArXiv,
SystematicReviews,7. abs/2007.15779.
Suchin Gururangan, Ana Marasovic, Swabha
IzBeltagy,KyleLo,andArmanCohan.2019. Scibert:
Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey,
A pretrained language model for scientific text. In
and Noah A. Smith. 2020. Don’t stop pretraining:
EMNLP.
Adapt language models to domains and tasks. In
Iz Beltagy, Matthew E. Peters, and Arman Cohan. ACL.
2020. Longformer: Thelong-documenttransformer.
Suchin Gururangan, Swabha Swayamdipta, Omer
ArXiv,abs/2004.05150.
Levy, Roy Schwartz, Samuel R. Bowman, and
Taylor Berg-Kirkpatrick, David Burkett, and Dan Noah A. Smith. 2018. Annotation artifacts in nat-
Klein. 2012. An empirical investigation of statisti- urallanguageinferencedata. InNAACL.
calsignificanceinnlp. InEMNLP.
AndreasHanselowski, ChristianStab, Claudia Schulz,
Zile Li, and Iryna Gurevych. 2019. A richly anno-
Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris
tated corpus for different tasks in automated fact-
Callison-Burch,andDanRoth.2019. Seeingthings
checking. InCoNLL.
fromadifferentangle: Discoveringdiverseperspec-
tivesaboutclaims. InNAACL.
Kokil Jaidka, Muthu Kumar Chandrasekaran, Devan-
shuJain,andMin-YenKan.2017. Thecl-scisumm
Arman Cohan, Luca Soldaini, and Nazli Goharian.
shared task 2017: Results and key insights. In
2015. Matching citation text and cited spans in
BIRNDL@JCDL.
biomedical literature: a search-oriented approach.
InNAACL.
QiaoJin,BhuwanDhingra,ZhengpingLiu,WilliamW.
Cohen, and Xinghua Lu. 2019. Pubmedqa: A
Kevin Bretonnel Cohen, Hoa Trang Dang, Anita
dataset for biomedical research question answering.
de Waard, Prabha Yadav, and Lucy Vanderwende.
InEMNLP.
2014. Tac 2014 biomedical summarization track.
https://tac.nist.gov/2014/BiomedSumm/. Eric Lehman, Jay DeYoung, Regina Barzilay, and By-
ron C. Wallace. 2019. Inferring which medical
Leon Derczynski, Kalina Bontcheva, Maria Liakata,
treatments work from reports of clinical trials. In
RobProcter,GeraldineWongSakHoi,andArkaitz
NAACL.
Zubiaga.2017. SemEval-2017task8: RumourEval:
Determining rumour veracity and support for ru- Tao Lei, Regina Barzilay, and Tommi S. Jaakkola.
mours. InSemEval. 2016. Rationalizingneuralpredictions. InACL.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and YinhanLiu,MyleOtt,NamanGoyal,JingfeiDu,Man-
KristinaToutanova.2019. Bert:Pre-trainingofdeep dar Joshi, Danqi Chen, Omer Levy, Mike Lewis,
bidirectional transformers for language understand- Luke Zettlemoyer, and Veselin Stoyanov. 2019.
ing. InNAACL. Roberta: A robustly optimized bert pretraining ap-
proach. ArXiv,abs/1907.11692.
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani,
EricLehman, CaimingXiong, RichardSocher, and KyleLo,LucyLuWang,MarkNeumann,RodneyKin-
Byron C. Wallace. 2020a. Eraser: A benchmark to ney,andDanielS.Weld.2020. S2ORC:TheSeman-
evaluaterationalizednlpmodels. InACL. ticScholarOpenResearchCorpus. InACL.
7543
Yi Luan, Luheng He, Mari Ostendorf, and Hannaneh Andreas Vlachos and Sebastian Riedel. 2014. Fact Hajishirzi. 2018. Multi-task identification of enti- checking: Task definition and dataset construction. ties, relations, and coreference for scientific knowl- In ACL Workshop on Language Technologies and
| edgegraphconstruction. | InEMNLP. | ComputationalSocialScience. | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| IainJamesMarshall,Joe¨lKuiper,EdwardBanner,and Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, | ||||||||||||
| RussellReas,JiangjiangYang,DarrinEide,Kathryn | ||||||||||||
| ByronC.Wallace.2017. | Automatingbiomedicalev- | |||||||||||
| -------------------- | --- | -------------- | ----------------------- | ---- | --- | --- | ------------ | ------- | ----- | ------ | ------------- | ------- |
| Funk, Rodney | Kinney, | Ziyang | Liu, William. | Mer- | ||||||||
| idencesynthesis: | Robotreviewer. | ACL. | ||||||||||
| rill, Paul | Mooney, | Dewey | A. | Murdick, | Devvret | |||||||
| Iain James Marshall and Byron C. Wallace. 2019. Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stil- | ||||||||||||
| Toward systematic review automation: a practical son,AlexD.Wade,KuansanWang,ChristopherWil- | ||||||||||||
| helm, Boya | Xie, | Douglas | M. | Raymond, | Daniel S. | |||||||
| -------- | ----- | ------- | -------- | --- | ----- | ----------- | ---------- | ---- | ------- | --- | -------- | --------- |
| guide to | using | machine | learning | tools | in research | |||||||
| Weld,OrenEtzioni,andSebastianKohlmeier.2020. | ||||||||||||
| synthesis. | SystematicReviews,8. | |||||||||||
| ---------- | -------------------- | --- | --- | --- | --- | --- | -------- | --- | -------- | ---- | -------- | -------- |
| Cord-19: | The | covid-19 | open | research | dataset. | |||||||
| ArXiv,abs/2004.10706. | ||||||||||||
| Preslav I | Nakov, | Ariel | S Schwartz, | and Marti | Hearst. | |||||||
| --------- | ------ | ----- | ----------- | --- | --------- | ------- | --- | --- | --- | --- | --- | --- |
| 2004. Citances: Citation sentences for semantic WilliamYangWang.2017. “liar,liarpantsonfire”: A | ||||||||||||
| analysis | of bioscience | text. | In SIGIR | workshop | on | |||||||
| -------- | ------------- | --- | ----- | -------- | -------- | --- | ---------------------------------------- | --- | --- | --- | --- | --- |
| newbenchmarkdatasetforfakenewsdetection. | In | |||||||||||
| SearchandDiscoveryinBioinformatics. | ||||||||||||
| ACL. | ||||||||||||
| Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Thomas Wolf, Lysandre Debut, Victor Sanh, Julien | ||||||||||||
| Yang, Iain James Marshall, Ani Nenkova, and By- Chaumond, ClementDelangue, AnthonyMoi, Pier- | ||||||||||||
| ronC.Wallace.2018. Acorpuswithmulti-levelan- ricCistac,TimRault,R’emiLouf,MorganFuntow- | ||||||||||||
| notationsofpatients,interventionsandoutcomesto icz, and Jamie Brew. 2019. Huggingface’s trans- | ||||||||||||
| support | language | processing | for medical | literature. | ||||||||
| ------- | -------- | ---------- | --- | ----------- | --- | ----------- | -------- | ---------------- | --- | ------- | -------- | -------- |
| formers: | State-of-the-art | natural | language | process- | ||||||||
| InACL. | ||||||||||||
| ing. ArXiv,abs/1910.03771. | ||||||||||||
| Kashyap | Popat, | Subhabrata | Mukherjee, | Jannik | ||||||||
| ----------------------------- | ------------------------------------ | ---------- | ------- | ----------- | ---------- | --------- | --- | --- | --- | --- | --- | --- |
| Stro¨tgen, | and | Gerhard | Weikum. | 2017. | Where the | |||||||
| truth lies: | Explaining | the | credibility | of | emerging | |||||||
| claimsonthewebandsocialmedia. | InWWW. | |||||||||||
| Tal Schuster, | Darsh | J. | Shah, | Yun | Jie Serene | Yeo, | ||||||
| Daniel | Filizzola, | Enrico | Santus, | and | Regina | Barzi- | ||||||
| lay.2019. | Towardsdebiasingfactverificationmod- | |||||||||||
| els. InEMNLP. | ||||||||||||
| Amir Soleimani, | Christof | Monz, | and | Marcel | Worring. | |||||||
| --------------- | ------------------------------------ | -------- | --------- | --- | --------- | -------- | --- | --- | --- | --- | --- | --- |
| 2019. | Bert for | evidence | retrieval | and claim | verifi- | |||||||
| cation. | InEuropeanConferenceonInformationRe- | |||||||||||
| trieval. | ||||||||||||
| James Thorne, | Andreas | Vlachos, | Christos | |||||||||
| ------------------- | -------------------------- | ----------- | ---------- | ------------ | -------------- | ---------- | --- | --- | --- | --- | --- | --- |
| Christodoulopoulos, | and | Arpit | Mittal. | 2018. | ||||||||
| Fever: | a large-scale | dataset | for fact | extraction | and | |||||||
| verification. | InNAACL. | |||||||||||
| Guy Tsafnat, | Paul | P Glasziou, | Miew | Keen | Choong, | |||||||
| Adam | G. Dunn, | Filippo | Galgani, | and | Enrico W. | |||||||
| Coiera. | 2014. | Systematic | review | automation | tech- | |||||||
| nologies. | SystematicReviews,3:74–74. | |||||||||||
| George Tsatsaronis, | Georgios | Balikas, | Prodromos | |||||||||
| Malakasiotis, | Ioannis | Partalas, | Matthias | Zschunke, | ||||||||
| Michael | R. Alvers, | Dirk | Weissenborn, | Anastasia | ||||||||
| Krithara, | Sergios | Petridis, | Dimitris | Polychronopou- | ||||||||
| los, Yannis | Almirantis, | John | Pavlopoulos, | Nico- | ||||||||
| las Baskiotis, | Patrick | Gallinari, | Thierry | Artie`res, | ||||||||
| Axel-Cyrille | Ngonga | Ngomo, | Norman | Heino, | E´ric | |||||||
| Gaussier,LilianaBarrio-Alvers,MichaelSchroeder, | ||||||||||||
| IonAndroutsopoulos,andGeorgiosPaliouras.2015. | ||||||||||||
| An overview | of the | bioasq | large-scale | biomedical | ||||||||
| ----------- | --- | ------ | ------ | ----------- | --- | ---------- | --- | --- | --- | --- | --- | --- |
| semanticindexingandquestionansweringcompeti- | ||||||||||||
| tion. InBMCBioinformatics. | ||||||||||||
| 7544 |
| A | Modelimplementationdetails | A.3 Trainingthe | LABELPREDICTION | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| module | |||||||||||||
| AllmodelsareimplementedusingtheHuggingface We adopt similar settings as we used for the RA- | |||||||||||||
| Transformerspackage(Wolfetal.,2019). TIONALESELECTION moduleandonlychangethe | |||||||||||||
| learningrateto1e-5forthetransformerbaseand | |||||||||||||
| 1e-4forthelinearlayerformodelstrainedon | SCI- | ||||||||||||
| --- | --------------------- | --- | --- | --- | ------------- | --- | --- | --------------------------------------- | --- | --- | --- | -------------- | ---- |
| A.1 | Parametersforthefinal | VERISCIsystem | |||||||||||
| FACT,FEVER,andUKPSnopes. | Whentrainingon | ||||||||||||
| claim/citedabstractpairslabeledNOINFO,weuse | |||||||||||||
| Forthe | ABSTRACTRETRIEVALmodule, | VERISCI | |||||||||||
| ------ | ------------------------ | --- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- |
| thek | |||||||||||||
| retrieves the top k = 3 documents ranked by TF- sentencesintheabstractwithgreatestsimi- | |||||||||||||
| laritytotheclaimasrationales(§5). | k issampled | ||||||||||||
| --- | ---------- | --- | ----- | ------- | -------- | --- | --------- | --------------------------------- | --- | --- | --- | ----------- | --- |
| IDF | similarity | using | unigram | + bigram | features. | ||||||||
| from{0,1}withuniformprobability. | |||||||||||||
| Theseparametersaretunedonthe | SCIFACTdevel- | ||||||||||||
| ---------------------------- | --- | ------ | ----------- | --- | ------------- | --- | ------ | ----------------------------- | --- | --- | --- | --- | --- |
| opmentset. | A.4 Additionaltrainingdetails | ||||||||||||
| When | making | predictions | using | the | RATIO- | ||||||||
| AllmodelsaretrainedusingasingleNvidiaP100 | |||||||||||||
| NALESELECTIONmoduledescribedin§5,wefind GPUonGoogleColabortoaryProplatform.8 | |||||||||||||
| For | |||||||||||||
| that | the | usual decision | rule | of predicting | zˆ = 1 | ||||||||
| -------- | --- | ------------------------------- | ----- | ---- | ------------- | ------- | ------ | ---------------------------------------- | --- | ----- | ---------- | -------------- | --- |
| i | theRATIONALESELECTIONmodule,ittakesabout | ||||||||||||
| when | z˜ | ≥ 0.5 | works | well | for models | trained | on | ||||||
| i | 150 minutes | to | train | on SCIFACT | for 20 epochs. | ||||||||
| SCIFACT. | However,formodelstrainedonFEVER | ||||||||||||
| 120minutesonUKPSnopesfor5epochs,and700 | |||||||||||||
| andUKPSnopes,weachievebetterperformance minuteson FEVER for3epochs. Forthe LABEL- | |||||||||||||
| by | tuning | the classification | threshold | t, such | that | ||||||||
| --- | ------ | ------------------ | --- | ----- | ----------- | ------- | -------- | ---------- | ----------------------------- | --- | -------------- | ----------- | --- |
| PREDICTION | module,ittakesabout130minutes | ||||||||||||
| zˆ | = 1 | when z˜ | ≥ | t, on | the SCIFACT | dev set. | |||||||
| i | i | to train | on SCIFACT | for 20 epochs, | 160 minutes | ||||||||
| The | best | threshold | was | t = | 0.025 | when | training | ||||||
| onUKPSnopesfor5epochs,and640minuteson | |||||||||||||
| on | FEVER, | and | t = | 0.75 when | training | on UKP | |||||||
| --- | ------ | --- | --- | --------- | -------- | --- | ------ | --- | --- | --- | --- | --- | --- |
| FEVERfor3epochs. | |||||||||||||
| Snopes. | |||||||||||||
| A.5 Hyperparametersearch | |||||||||||||
| --- | --- | --- | --- | --- | --- | --- | --- | ------------------------ | --- | --- | --- | --- | --- |
| Thelearningrate,batchsize,andnumberofepochs | |||||||||||||
| A.2 | Trainingthe | RATIONALESELECTION | |||||||||||
| --- | ----------- | --- | ------------------ | --- | --- | --- | --- | ----------------------------------- | --- | --- | --- | ------ | --- |
| arethemostimportanthyperparameters. | Weper- | ||||||||||||
| module | |||||||||||||
| formmanualtuningandselectthehyperparameters | |||||||||||||
| that produce | the | highest | F1 on | the development | |||||||||
| --- | ---------- | --- | ---- | ------- | -------- | ----- | ---- | --------------------------------------------- | --- | ------- | ----- | --------------- | --- |
| We | experiment | with | various | learning | rates | when | |||||||
| set. Forthelearningrate,weexperimentwith1e-3, | |||||||||||||
| training SCIBERT,BioMedRoBERTa,RoBERTa- | |||||||||||||
| base,andRoBERTa-large. Belowwedescribethe 1e-4, 5e-5, 1e-5, and5e-6. Forbatchsize, weex- | |||||||||||||
| settingfortrainingRoBERTa-large. perimentwith64and256. Thenumberofepochs | |||||||||||||
| arecutoffafterthemodelconverges. | |||||||||||||
| Formodelstrainedon | SCIFACT,weuseanini- | ||||||||||||
| ------------------ | -------- | ------ | ------- | ------------------- | --------------- | ----- | ------ | --------------------- | --------------- | --- | ------ | ---------------- | --- |
| tial | learning | rate | of 1e-5 | on | the transformer | base | |||||||
| B Statisticalanalysis | |||||||||||||
| and | 1e-3 | on the | linear | layer. | For | FEVER | + SCI- | ||||||
| We assess | the uncertainty | in the | results reported | ||||||||||
| FACT,thelearningrateissetto1e-5fortheentire | |||||||||||||
| modelforpre-trainingon FEVER andfine-tuning inthemainresults(Table4)usingasimpleboot- | |||||||||||||
| on SCIFACT. Weuseabatchsizeof256through strapapproach(Droretal.,2018;Berg-Kirkpatrick | |||||||||||||
| et al., 2012; | Efron | and | Tibshirani, | 1993). | Given | ||||||||
| -------- | --- | ------------ | --- | --- | ------------ | --- | -------- | ------------- | ----- | --- | ----------- | ------ | ----- |
| gradient | accumulation | and | apply cosine | learning | |||||||||
| ratedecayover20epochstofindthebestperform- our test set with n = 300 claims, we gener- | |||||||||||||
| test | |||||||||||||
| ingmodelonthedevset. ate n = 10,000 bootstrap-resampled test sets | |||||||||||||
| boot | |||||||||||||
| byresampling(uniformly,withreplacement)n | |||||||||||||
| Formodelstrainedon | FEVER,wesetthelearn- | test | |||||||||||
| ------------------ | --- | --- | --- | -------------------- | --- | --- | --- | --------------------- | --- | --- | ------------------------ | --- | ---- |
| claimsfromthetestset. | Foreachresampledtestset, | ||||||||||||
| ingrateto5e-6forthetransformerbaseand5e-5 | |||||||||||||
| wecomputethemetricsinTable4. | Table6reports | ||||||||||||
| --- | ---------- | ------ | --- | ---------- | ------- | --- | ------ | ---------------------------- | --- | --- | --- | ------------- | --- |
| for | the linear | layer. | For models | trained | on UKP | ||||||||
| themeanandstandarddeviationofthesemetrics, | |||||||||||||
| Snopes,wesetthelearningrate1e-5forthetrans- | |||||||||||||
| computedoverthebootstrapsamples. | Table7re- | ||||||||||||
| ----------------------------------- | --- | --- | --- | --- | --- | --- | ------ | -------------------------------- | --- | --- | ------------------------- | --------- | --- |
| formerbaseand1e-4forthelinearlayer. | Wefind | ||||||||||||
| portsdevsetmetrics. | Ourconclusionthattraining | ||||||||||||
| thattheselearningrateshelpthemodelsconverge. | |||||||||||||
| onSCIFACTimprovesperformanceisrobusttothe | |||||||||||||
| We | only | train the | model | for | 3 epochs | on | FEVER | ||||||
| --- | ---- | --------- | ----- | --- | -------- | --- | ----- | --- | --- | --- | --- | --- | --- |
| uncertaintiespresentedinthesetables. | |||||||||||||
| and | 5 epochs | on | UKP | Snopes | because | they are | |||||||
| ------------ | -------- | --- | ------- | ------ | --------- | --- | -------- | ----------------------------------- | --- | --- | --- | --- | --- |
| larger | datasets | and the | models | converged | within | ||||||||
| earlyepochs. | 8https://colab.research.google.com/ | ||||||||||||
| 7545 |
Sentence-level
| Selection-Only | Selection+Label | |||||||
|---|---|---|---|---|---|---|---|---|
| Retrieval | Model | Row | P | R | F1 | P | R | F1 |
| Oraclerationale | 1 | 100.0 | 80.5 | 89.2 | 89.6 | 72.2 | 79.9 | |
| 0.0 | 3.3 | 2.1 | 2.7 | 3.7 3.0 | ||||
| Oracle | ||||||||
| Zero-shot | 2 | 42.6 | 45.2 | 43.8 | 36.2 | 38.4 | 37.2 | |
| -------- | --------------- | --- | ----- | ---- | ---- | ---- | ---- | ------- |
| abstract | 2.2 | 3.2 | 2.0 | 2.5 | 3.0 2.3 | |||
| VERISCI | 3 | 76.2 | 63.9 | 69.4 | 66.5 | 55.7 | 60.6 | |
| 2.9 | 3.6 | 2.6 | 3.4 | 3.7 3.1 | ||||
| Oraclerationale | 4 | 100.0 | 56.6 | 72.2 | 87.6 | 49.5 | 63.2 | |
| 0.0 | 4.0 | 3.3 | 3.5 | 3.9 3.7 | ||||
| Open | Zero-shot | 5 | 28.7 | 37.6 | 32.5 | 23.8 | 31.1 | 26.9 |
| 2.3 | 3.4 | 2.3 | 2.3 | 3.1 2.3 | ||||
| VERISCI | 6 | 45.0 | 47.4 | 46.1 | 38.5 | 40.6 | 39.5 | |
| 3.0 | 3.8 | 3.0 | 3.0 | 3.6 3.0 | ||||
| (a)Sentence-levelresults. | ||||||||
| Abstract-level | ||||||||
| Label-Only | Label+Rationale | |||||||
| --------- | --------------- | --- | ---- | ---------- | ---- | ---- | --------------- | ------- |
| Retrieval | Model | Row | P | R | F1 | P | R | F1 |
| 90.1 | 77.5 | 83.3 | 90.1 | 77.5 | 83.3 | |||
| Oraclerationale | 1 | 2.2 | 2.8 | 2.4 | 2.2 | 2.8 2.4 | ||
| Oracle | ||||||||
| abstract Zero-shot 2 86.9 2.9 53.6 3.4 66.3 3.1 67.9 3.9 41.9 3.2 51.8 3.4 | ||||||||
| VERISCI | 3 | 87.3 | 2.6 65.3 | 3.2 74.7 | 2.8 84.9 | 2.8 63.5 | 3.2 72.6 2.9 | |
| --- | --------------- | --- | ---- | -------- | -------- | -------- | -------- | ------------ |
| Oraclerationale | 4 | 88.9 | 2.7 54.1 | 3.5 67.2 | 3.2 88.9 | 2.7 54.1 | 3.5 67.2 3.2 | |
| Open Zero-shot 5 56.0 3.9 42.3 3.4 48.2 3.3 42.3 4.0 32.0 3.2 36.4 3.3 | ||||||||
| VERISCI | 6 | 47.5 | 3.3 47.3 | 3.5 47.4 | 3.1 46.6 | 3.3 46.4 | 3.5 46.4 3.1 | |
| --- | ------- | --- | ---- | -------- | -------- | -------- | -------- | ------------ |
| (b)Abstract-levelresults | ||||||||
| Table 6: Test set results as in Table 4, reporting mean and standard deviation over 10,000 bootstrap samples. | ||||||||
| Standarddeviationsarereportedassubscripts. SomemeansreportedhereareslightlydifferentfromTable4due | ||||||||
| tosamplingvariability. | ||||||||
| Sentence-level | ||||||||
| Selection-Only | Selection+Label | |||||||
| --------- | --------------- | --- | ----- | -------------- | ---- | ---- | --------------- | ------- |
| Retrieval | Model | Row | P | R | F1 | P | R | F1 |
| Oraclerationale | 1 | 100.0 | 81.9 | 90.0 | 91.4 | 74.9 | 82.3 | |
| 0.0 | 3.2 | 1.9 | 2.5 | 3.6 2.9 | ||||
| Oracle | ||||||||
| Zero-shot | 2 | 40.7 | 48.1 | 44.0 | 36.1 | 42.6 | 39.0 | |
| -------- | --------------- | --- | ----- | ---- | ---- | ---- | ---- | ------- |
| abstract | 2.1 | 3.4 | 2.1 | 2.5 | 3.4 2.5 | |||
| VERISCI | 3 | 79.4 | 59.0 | 67.7 | 71.4 | 53.0 | 60.8 | |
| 2.7 | 3.6 | 2.8 | 3.5 | 3.6 3.3 | ||||
| Oraclerationale | 4 | 100.0 | 58.4 | 73.7 | 90.2 | 52.7 | 66.4 | |
| 0.0 | 4.3 | 3.4 | 3.3 | 4.3 3.9 | ||||
| Open | Zero-shot | 5 | 28.6 | 38.5 | 32.8 | 24.8 | 33.4 | 28.4 |
| 2.0 | 3.6 | 2.3 | 2.2 | 3.4 2.4 | ||||
| VERISCI | 6 | 52.5 | 43.8 | 47.7 | 46.9 | 39.2 | 42.6 | |
| 3.5 | 3.7 | 3.2 | 3.7 | 3.6 3.2 | ||||
| (a)Sentence-levelresults. | ||||||||
| Abstract-level | ||||||||
| Label-Only | Label+Rationale | |||||||
| --------- | --------------- | --- | ---- | ---------- | -------- | -------- | --------------- | ------------ |
| Retrieval | Model | Row | P | R | F1 | P | R | F1 |
| Oraclerationale | 1 | 91.4 | 2.2 76.1 | 3.0 83.0 | 2.5 91.4 | 2.2 76.1 | 3.0 83.0 2.5 | |
| Oracle | ||||||||
| Zero-shot | 2 | 88.9 | 2.8 58.3 | 3.7 70.4 | 3.2 69.2 | 3.9 45.4 | 3.5 54.8 3.5 | |
| --- | --------- | --- | ---- | -------- | -------- | -------- | -------- | ------------ |
| abstract | ||||||||
| VERISCI | 3 | 91.0 | 2.3 67.4 | 3.3 77.4 | 2.7 85.2 | 2.9 63.2 | 3.5 72.5 3.1 | |
| ---- | --------------- | --- | ---- | -------- | -------- | -------- | -------- | ------------ |
| Oraclerationale | 4 | 91.0 | 2.6 53.1 | 3.8 67.0 | 3.4 91.0 | 2.6 53.1 | 3.8 67.0 3.4 | |
| Open | Zero-shot | 5 | 52.7 | 41.6 | 46.5 | 43.6 | 34.4 | 38.4 |
| 3.7 | 3.7 | 3.4 | 3.7 | 3.5 3.3 | ||||
| VERISCI | 6 | 55.4 | 47.5 | 51.0 | 52.6 | 45.1 | 48.5 | |
| 3.7 | 3.6 | 3.3 | 3.7 | 3.6 3.3 | ||||
| (b)Abstract-levelresults | ||||||||
| Table7: DevsetresultsasinTable4,reportingmeanandstandarddeviationover10,000bootstrapsamples. | ||||||||
| 7546 |
| Journal | Count | Source citance | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BMJ | 60 | |||||||||||
| "Future studies are also warranted to evaluate | ||||||||||||
| the potential association between WNT5A/PCP | ||||||||||||
| Blood | 8 | |||||||||||
| ----- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| signaling in adipose tissue and | ||||||||||||
| CancerCell | 8 | atherosclerotic CVD, given the major role that | ||||||||||
| ---------- | --- | --- | --- | --- | --- | --- | ----------------------------------------------- | --- | --- | --- | --- | --- |
| Cell | 51 | IL-6 signaling plays in this condition as | ||||||||||
| revealed by large Mendelian randomization | ||||||||||||
| CellMetabolism | 10 | studies 44, 45 | ." | |||||||||
| -------------- | --- | --- | --- | --- | --- | --- | -------------- | --- | --- | --- | --- | --- |
| CellStemCell | 41 | |||||||||||
| Claim | ||||||||||||
| Circulation | 12 | |||||||||||
| ----------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| IL-6 signaling plays a major role in | ||||||||||||
| Immunity | 33 | |||||||||||
| -------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| atherosclerotic cardiovascular disease. | ||||||||||||
| JAMA | 79 | |||||||||||
| ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| MolecularCell 27 Figure 4: A claim written based on a citance. Mate- | ||||||||||||
| MolecularSystemsBiology 5 rialunrelatedtothecitationisremoved. Theacronym | ||||||||||||
| “CVD”isexpandedto“cardiovasculardisease”. | ||||||||||||
| Nature | 29 | |||||||||||
| ----------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| NatureCellBiology | 26 | |||||||||||
| NatureCommunications 19 ten. Theseretrievalswouldbeincorrectlymarked | ||||||||||||
| NatureGenetics | 8 | |||||||||||
| -------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| wrongbyourevaluationmetrics. | ||||||||||||
| NatureMedicine | 89 | |||||||||||
| -------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Distractorabstractsasdefinedin§3.1havetwo | ||||||||||||
| NatureMethods | 1 | |||||||||||
| -------------------- | --- | --- | --- | --- | --- | --- | --------- | ------- | --------- | ---------- | ------------- | ------------ |
| qualities | that | make them | a | good addition | to the | |||||||
| NucleicAcidsResearch | 10 | |||||||||||
| SCIFACT | corpus: | (1) | They | are cited | in the same | |||||||
| PlosBiology | 36 | |||||||||||
| articles | as our | evidence | abstracts, | meaning that | ||||||||
| PlosMedicine | 38 | |||||||||||
| theyoftendiscusssimilartopicsandincreasethe | ||||||||||||
| Science | 7 | difficulty | of | abstract retrieval | methods | based on | ||||||
| ---------------------------- | --- | --- | --- | --- | --- | --- | ------------------------------------------- | ---------------------------------- | -------------------------- | --- | ---------- | --------- |
| ScienceTranslationalMedicine | 2 | |||||||||||
| lexicalsimilarity. | (2)Theauthorsofourcitances | |||||||||||
| TheLancet | 22 | |||||||||||
| were | aware of | the distractor | abstracts, | and chose | ||||||||
| Other | 120 | nottomentiontheminthecitancesusedtogenerate | ||||||||||
| claims. | Thismakesthemunlikelytobeasourceof | |||||||||||
| Total | 741 | |||||||||||
| falsenegativeretrievals. | ||||||||||||
| Table8: | Numberofciteddocumentsbyjournal. | Some | ||||||||||
| ------- | -------------------------------- | --- | --- | --- | --- | ---- | --- | ------------------ | --- | --- | --- | --- |
| C.2 | Annotationexamples | |||||||||||
| co-citedarticles(§3.1)comefromjournalsoutsideour | ||||||||||||
| curatedset;theseareindicatedby“Other”. Converting citances to claims Figure 4 shows | ||||||||||||
| an example | of a citance | re-written | as a claim. | |||||||||
| --- | --- | --- | --- | --- | --- | --- | ----------- | --- | ------------ | ---------- | ------------ | ----------- |
| The citance | discusses | the | relationship | between | ||||||||
| C Datasetcollectionandcorpusstatistics | ||||||||||||
| “atheroscleroticCVD”and“IL-6”,andcitestwopa- | ||||||||||||
| C.1 Corpus | pers(44and45)asevidence. | Toconverttoaclaim, | ||||||||||
| ---------- | --- | --- | --- | --- | --- | --- | ------------------------ | --- | --- | --- | ------------------ | --- |
| theacronym“CVD”isexpandedto“cardiovascu- | ||||||||||||
| Source | journals | Table | 8 shows | the number | of | |||||||
| ------ | -------- | ----- | ------- | --- | ---------- | --- | --- | --- | --- | --- | --- | --- |
| lardisease”,irrelevantinformationisremoved,and | ||||||||||||
| citedabstractsfromeachofourselectedjournals. | ||||||||||||
| theclaimiswrittenasanatomicfactualstatement. | ||||||||||||
| The “Other” | category | includes | “co-cited” | (§3.1) | ||||||||
| ----------- | -------- | --------- | -------- | ---------- | --------- | ------ | ------------------ | --- | --- | ---------------------- | --- | --- |
| abstracts | that | came from | journals | not among | our | |||||||
| Multiplerationales | Figure5showsaclaimsup- | |||||||||||
| pre-definedset. | ||||||||||||
| ported | by two | rationales | from | the same | abstract. | |||||||
| --- | --- | --- | --- | --- | --- | --- | ------ | ------ | ---------- | ---- | -------- | --------- |
| Thetextofeachrationaleonitsownissufficientto | ||||||||||||
| Distractor | abstracts | In §3.1, | we | mention | how | |||||||
| ----------- | ---------- | ------- | ---------- | --- | --------- | ---- | --------------- | --------------------------- | --- | --- | --- | --- |
| we increase | the | size of | the corpus | by adding | dis- | entailtheclaim. | ||||||
| tractor | abstracts. | The | reason | why | we do not | use | ||||||
| C.3 | Annotatorsandqualitycontrol | |||||||||||
| theentiretyofalargeresearchcorpuslikeS2ORC | ||||||||||||
| asourfact-checkingcorpusisthatdoingsowould Claimwriting Studentclaimwritersattendedan | ||||||||||||
| introducemanyfalsenegativeretrievals: abstracts in-person training session where they were intro- | ||||||||||||
| containingevidencerelevanttoagivenclaim,but ducedtothetaskandreceivedin-personfeedback | ||||||||||||
| notmentionedintheclaim’ssourcecitance. This fromthefourexperts. Followingtraining,student | ||||||||||||
| canoccureitherbecausethecitanceauthorssimply annotatorscontinuedwritingclaimsremotely. The | ||||||||||||
| were not aware of these abstracts, or because the expertannotatorsmonitoredclaimsforqualitydur- | ||||||||||||
| abstractswerepublishedafterthecitancewaswrit- ing the remote annotation process, and provided | ||||||||||||
| 7547 |
https://scifact.s3-us-west-2.amazonaws. Claim Antibiotic induced alterations in the gut com/doc/evidence-annotation-instructions. microbiome reduce resistance against pdf. Clostridium difficile
| Decision: | SUPPORTS | ||||
|---|---|---|---|---|---|
| Antibiotics can have significant and long- | |||||
| lasting effects on the gastrointestinal tract | |||||
| microbiota, reducing colonization resistance | |||||
| against pathogens including Clostridium | |||||
| difficile. | |||||
| Rationale 1 | |||||
| Our results indicate that antibiotic-mediated | |||||
| alteration of the gut microbiome converts the | |||||
| global metabolic profile to one that favours | |||||
| C. difficile germination and growth. | |||||
| Rationale 2 | |||||
| Figure5:Aclaimsupportedbytworationalesfromthe | |||||
| same abstract. | The | text | of each | rationale | on its own |
| -------------- | --- | ---- | ------- | --------- | ---------- |
| providessufficientevidencetoverifytheclaim. | |||||
| feedbackwhennecessary;low-qualityclaimswere | |||||
| returnedtotheannotatorsforre-writing. | Asafinal | ||||
| ------------------------------------- | ---------- | --- | ------ | -------------- | -------- |
| check, all | submitted | claims | were proofread | (and | |
| edited if | necessary) | by | an | undergraduate | whose |
| claimsweredeemedespeciallyhigh-qualitybythe | |||||
| expertannotators. | |||||
| Claim negations | As | mentioned | in §3.2, | an ex- | |
| --------------- | --- | --- | --------- | -------- | ------ |
| pertannotatorwroteclaimnegationstointroduce | |||||
| caseswhereanabstractREFUTESaclaim. | Thean- | ||||
| ---------------------------------- | --- | --- | --- | --- | ------ |
| notatorskippedclaimsthatcouldonlybenegated | |||||
| by adding | obvious | triggers | like “not”. | The ma- | |
| --------- | ------- | --------- | -------- | ----------- | ----------- |
| jority of | claim | negations | involved | a | reversal of |
| effectdirection;forinstance“Ahighmicroerythro- | |||||
| cytecountprotectsagainstsevereanemia”canbe | |||||
| negatedas“Ahighmicroerythrocytecountraises | |||||
| vulnerabilitytosevereanemia”. | |||||
| Claimverification | Annotationswereperformed | ||||
| ----------------------------- | ------- | ------------- | ------------------------ | -------------- | ---------- |
| remotelythroughawebinterface. | Annotatorswere | ||||
| required | to pass | a 10-question | “quiz” | before an- | |
| notatingtheirownclaims. | Afterpassingthequiz, | ||||
| subsequentsubmissionswerereviewedbyanNLP | |||||
| expertuntilthatexpertdeemedtheannotatorreli- | |||||
| able. Approvedannotatorswerethenassignedto | |||||
| revieweachothers’submissions. | Ingeneral,grad- | ||||
| ----------------------------- | --- | --- | --- | --------------- | --- |
| uatestudentswereassignedtoreviewannotations | |||||
| fromundergraduates. | |||||
| D Annotationinterfacesandguidelines | |||||
| Weshowascreenshotoftheclaimwritinginterface | |||||
| inFigure6,andtheclaimverificationinterfacein | |||||
| Figure7. | Thecompleteannotationguideforclaim | ||||
| ------------ | ---------------------------------- | --------- | --- | ------------- | ---- |
| verification | is | available | at | the following | URL: |
| 7548 |
Figure 6: The claim-writing interface. The citation sentence is highlighted in blue on the top left. Additional context is provided on bottom left. The right side shows two claims that could be written based on this citation sentence. 7549
Figure7: Theevidencecollectioninterface. 7550