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| --- | ------------------- | --- | ---- | ------------- | ----------- | ---------- | -------------------- | ----------- | --- | --- | --- | --- |
| | 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
1. ABSTRACTRETRIEVAL retrieves k abstracts
atidentifyingthesentencessufficienttojustifythe
withhighestTF-IDFsimilaritytotheclaim.
abstract-levelpredictions. Weconductevaluations
2. RATIONALESELECTION identifies rationale
in both the “Open” FEVER-style (Thorne et al.,
sentencesS(cid:98)(c,a)foreachabstract.
2018) setting where the evidence abstracts must
3. 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
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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/ | | | | | |
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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.
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| 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: |
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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.
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Figure7: Theevidencecollectioninterface.
7550 |