Datasets:

luoojason
/

muddle-eval-bundle-md

	\| \| \| \| 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
	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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	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.
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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: \|
	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