| | | | 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 References Jay DeYoung, Eric Lehman, Ben Nye, Iain James Marshall, and Byron C. Wallace. 2020b. Evi- Ramy Baly, Mitra Mohtarami, James Glass, Llu´ıs dence inference 2.0: More data, better models. In Ma`rquez,AlessandroMoschitti,andPreslavNakov. BioNLP@ACL. 2018. Integratingstancedetectionandfactchecking inaunifiedcorpus. InNAACL. RotemDror,GiliBaumer,SegevShlomov,andRoiRe- ichart.2018. 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InBMCBioinformatics. 7544 | A | Modelimplementationdetails | | | | | | | A.3 Trainingthe | | LABELPREDICTION | | | | | --- | -------------------------- | --- | --- | --- | --- | --- | --- | --------------- | --- | --------------- | --- | --- | --- | module AllmodelsareimplementedusingtheHuggingface We adopt similar settings as we used for the RA- Transformerspackage(Wolfetal.,2019). TIONALESELECTION moduleandonlychangethe learningrateto1e-5forthetransformerbaseand | | | | | | | | | 1e-4forthelinearlayerformodelstrainedon | | | | | SCI- | | --- | --------------------- | --- | --- | --- | ------------- | --- | --- | --------------------------------------- | --- | --- | --- | -------------- | ---- | | A.1 | Parametersforthefinal | | | | VERISCIsystem | | | | | | | | | | | | | | | | | | FACT,FEVER,andUKPSnopes. | | | | Whentrainingon | | claim/citedabstractpairslabeledNOINFO,weuse | Forthe | ABSTRACTRETRIEVALmodule, | | | | | VERISCI | | | | | | | | | ------ | ------------------------ | --- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | thek retrieves the top k = 3 documents ranked by TF- sentencesintheabstractwithgreatestsimi- | | | | | | | | | laritytotheclaimasrationales(§5). | | | | k issampled | | | --- | ---------- | --- | ----- | ------- | -------- | --- | --------- | --------------------------------- | --- | --- | --- | ----------- | --- | | IDF | similarity | | using | unigram | + bigram | | features. | | | | | | | from{0,1}withuniformprobability. | Theseparametersaretunedonthe | | | | | SCIFACTdevel- | | | | | | | | | | ---------------------------- | --- | ------ | ----------- | --- | ------------- | --- | ------ | ----------------------------- | --- | --- | --- | --- | --- | | opmentset. | | | | | | | | A.4 Additionaltrainingdetails | | | | | | | When | | making | predictions | | using | the | RATIO- | | | | | | | AllmodelsaretrainedusingasingleNvidiaP100 NALESELECTIONmoduledescribedin§5,wefind GPUonGoogleColabortoaryProplatform.8 For | that | the | usual decision | | rule | of predicting | | zˆ = 1 | | | | | | | | -------- | --- | ------------------------------- | ----- | ---- | ------------- | ------- | ------ | ---------------------------------------- | --- | ----- | ---------- | -------------- | --- | | | | | | | | | i | theRATIONALESELECTIONmodule,ittakesabout | | | | | | | when | z˜ | ≥ 0.5 | works | well | for models | trained | on | | | | | | | | | i | | | | | | | 150 minutes | to | train | on SCIFACT | for 20 epochs. | | | SCIFACT. | | However,formodelstrainedonFEVER | | | | | | | | | | | | 120minutesonUKPSnopesfor5epochs,and700 andUKPSnopes,weachievebetterperformance minuteson FEVER for3epochs. Forthe LABEL- | by | tuning | the classification | | | threshold | t, such | that | | | | | | | | --- | ------ | ------------------ | --- | ----- | ----------- | ------- | -------- | ---------- | ----------------------------- | --- | -------------- | ----------- | --- | | | | | | | | | | PREDICTION | module,ittakesabout130minutes | | | | | | zˆ | = 1 | when z˜ | ≥ | t, on | the SCIFACT | | dev set. | | | | | | | | i | | | i | | | | | to train | on SCIFACT | | for 20 epochs, | 160 minutes | | | The | best | threshold | was | t = | 0.025 | when | training | | | | | | | onUKPSnopesfor5epochs,and640minuteson | on | FEVER, | and | t = | 0.75 when | training | | on UKP | | | | | | | | --- | ------ | --- | --- | --------- | -------- | --- | ------ | --- | --- | --- | --- | --- | --- | FEVERfor3epochs. Snopes. | | | | | | | | | A.5 Hyperparametersearch | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | ------------------------ | --- | --- | --- | --- | --- | Thelearningrate,batchsize,andnumberofepochs | A.2 | Trainingthe | | RATIONALESELECTION | | | | | | | | | | | | --- | ----------- | --- | ------------------ | --- | --- | --- | --- | ----------------------------------- | --- | --- | --- | ------ | --- | | | | | | | | | | arethemostimportanthyperparameters. | | | | Weper- | | module formmanualtuningandselectthehyperparameters | | | | | | | | | that produce | the | highest | F1 on | the development | | | --- | ---------- | --- | ---- | ------- | -------- | ----- | ---- | --------------------------------------------- | --- | ------- | ----- | --------------- | --- | | We | experiment | | with | various | learning | rates | when | | | | | | | | | | | | | | | | set. Forthelearningrate,weexperimentwith1e-3, | | | | | | training SCIBERT,BioMedRoBERTa,RoBERTa- base,andRoBERTa-large. Belowwedescribethe 1e-4, 5e-5, 1e-5, and5e-6. Forbatchsize, weex- settingfortrainingRoBERTa-large. perimentwith64and256. Thenumberofepochs arecutoffafterthemodelconverges. | Formodelstrainedon | | | | SCIFACT,weuseanini- | | | | | | | | | | | ------------------ | -------- | ------ | ------- | ------------------- | --------------- | ----- | ------ | --------------------- | --------------- | --- | ------ | ---------------- | --- | | tial | learning | rate | of 1e-5 | on | the transformer | | base | | | | | | | | | | | | | | | | B Statisticalanalysis | | | | | | | and | 1e-3 | on the | linear | layer. | For | FEVER | + SCI- | | | | | | | | | | | | | | | | We assess | the uncertainty | | in the | results reported | | FACT,thelearningrateissetto1e-5fortheentire modelforpre-trainingon FEVER andfine-tuning inthemainresults(Table4)usingasimpleboot- on SCIFACT. Weuseabatchsizeof256through strapapproach(Droretal.,2018;Berg-Kirkpatrick | | | | | | | | | et al., 2012; | Efron | and | Tibshirani, | 1993). | Given | | -------- | --- | ------------ | --- | --- | ------------ | --- | -------- | ------------- | ----- | --- | ----------- | ------ | ----- | | gradient | | accumulation | | and | apply cosine | | learning | | | | | | | ratedecayover20epochstofindthebestperform- our test set with n = 300 claims, we gener- test ingmodelonthedevset. ate n = 10,000 bootstrap-resampled test sets boot byresampling(uniformly,withreplacement)n | Formodelstrainedon | | | | FEVER,wesetthelearn- | | | | | | | | | test | | ------------------ | --- | --- | --- | -------------------- | --- | --- | --- | --------------------- | --- | --- | ------------------------ | --- | ---- | | | | | | | | | | claimsfromthetestset. | | | Foreachresampledtestset, | | | ingrateto5e-6forthetransformerbaseand5e-5 | | | | | | | | | wecomputethemetricsinTable4. | | | | Table6reports | | | --- | ---------- | ------ | --- | ---------- | ------- | --- | ------ | ---------------------------- | --- | --- | --- | ------------- | --- | | for | the linear | layer. | | For models | trained | | on UKP | | | | | | | themeanandstandarddeviationofthesemetrics, Snopes,wesetthelearningrate1e-5forthetrans- | | | | | | | | | computedoverthebootstrapsamples. | | | | Table7re- | | | ----------------------------------- | --- | --- | --- | --- | --- | --- | ------ | -------------------------------- | --- | --- | ------------------------- | --------- | --- | | formerbaseand1e-4forthelinearlayer. | | | | | | | Wefind | | | | | | | | | | | | | | | | portsdevsetmetrics. | | | Ourconclusionthattraining | | | thattheselearningrateshelpthemodelsconverge. onSCIFACTimprovesperformanceisrobusttothe | We | only | train the | model | for | 3 epochs | on | FEVER | | | | | | | | --- | ---- | --------- | ----- | --- | -------- | --- | ----- | --- | --- | --- | --- | --- | --- | uncertaintiespresentedinthesetables. | and | 5 epochs | on | UKP | Snopes | because | | they are | | | | | | | | ------------ | -------- | --- | ------- | ------ | --------- | --- | -------- | ----------------------------------- | --- | --- | --- | --- | --- | | larger | datasets | | and the | models | converged | | within | | | | | | | | earlyepochs. | | | | | | | | 8https://colab.research.google.com/ | | | | | | 7545 Sentence-level | | | | | Selection-Only | | | Selection+Label | | | --------- | --------------- | --- | ----- | -------------- | ---- | ---- | --------------- | ------- | | Retrieval | Model | Row | P | R | F1 | P | R | F1 | | | Oraclerationale | 1 | 100.0 | 80.5 | 89.2 | 89.6 | 72.2 | 79.9 | | | | | | 0.0 | 3.3 | 2.1 | 2.7 | 3.7 3.0 | Oracle | | Zero-shot | 2 | 42.6 | 45.2 | 43.8 | 36.2 | 38.4 | 37.2 | | -------- | --------------- | --- | ----- | ---- | ---- | ---- | ---- | ------- | | abstract | | | | 2.2 | 3.2 | 2.0 | 2.5 | 3.0 2.3 | | | VERISCI | 3 | 76.2 | 63.9 | 69.4 | 66.5 | 55.7 | 60.6 | | | | | | 2.9 | 3.6 | 2.6 | 3.4 | 3.7 3.1 | | | Oraclerationale | 4 | 100.0 | 56.6 | 72.2 | 87.6 | 49.5 | 63.2 | | | | | | 0.0 | 4.0 | 3.3 | 3.5 | 3.9 3.7 | | Open | Zero-shot | 5 | 28.7 | 37.6 | 32.5 | 23.8 | 31.1 | 26.9 | | | | | | 2.3 | 3.4 | 2.3 | 2.3 | 3.1 2.3 | | | VERISCI | 6 | 45.0 | 47.4 | 46.1 | 38.5 | 40.6 | 39.5 | | | | | | 3.0 | 3.8 | 3.0 | 3.0 | 3.6 3.0 | (a)Sentence-levelresults. Abstract-level | | | | | Label-Only | | | Label+Rationale | | | --------- | --------------- | --- | ---- | ---------- | ---- | ---- | --------------- | ------- | | Retrieval | Model | Row | P | R | F1 | P | R | F1 | | | | | 90.1 | 77.5 | 83.3 | 90.1 | 77.5 | 83.3 | | | Oraclerationale | 1 | | 2.2 | 2.8 | 2.4 | 2.2 | 2.8 2.4 | Oracle abstract Zero-shot 2 86.9 2.9 53.6 3.4 66.3 3.1 67.9 3.9 41.9 3.2 51.8 3.4 | | VERISCI | 3 | 87.3 | 2.6 65.3 | 3.2 74.7 | 2.8 84.9 | 2.8 63.5 | 3.2 72.6 2.9 | | --- | --------------- | --- | ---- | -------- | -------- | -------- | -------- | ------------ | | | Oraclerationale | 4 | 88.9 | 2.7 54.1 | 3.5 67.2 | 3.2 88.9 | 2.7 54.1 | 3.5 67.2 3.2 | Open Zero-shot 5 56.0 3.9 42.3 3.4 48.2 3.3 42.3 4.0 32.0 3.2 36.4 3.3 | | VERISCI | 6 | 47.5 | 3.3 47.3 | 3.5 47.4 | 3.1 46.6 | 3.3 46.4 | 3.5 46.4 3.1 | | --- | ------- | --- | ---- | -------- | -------- | -------- | -------- | ------------ | (b)Abstract-levelresults Table 6: Test set results as in Table 4, reporting mean and standard deviation over 10,000 bootstrap samples. Standarddeviationsarereportedassubscripts. SomemeansreportedhereareslightlydifferentfromTable4due tosamplingvariability. Sentence-level | | | | | Selection-Only | | | Selection+Label | | | --------- | --------------- | --- | ----- | -------------- | ---- | ---- | --------------- | ------- | | Retrieval | Model | Row | P | R | F1 | P | R | F1 | | | Oraclerationale | 1 | 100.0 | 81.9 | 90.0 | 91.4 | 74.9 | 82.3 | | | | | | 0.0 | 3.2 | 1.9 | 2.5 | 3.6 2.9 | Oracle | | Zero-shot | 2 | 40.7 | 48.1 | 44.0 | 36.1 | 42.6 | 39.0 | | -------- | --------------- | --- | ----- | ---- | ---- | ---- | ---- | ------- | | abstract | | | | 2.1 | 3.4 | 2.1 | 2.5 | 3.4 2.5 | | | VERISCI | 3 | 79.4 | 59.0 | 67.7 | 71.4 | 53.0 | 60.8 | | | | | | 2.7 | 3.6 | 2.8 | 3.5 | 3.6 3.3 | | | Oraclerationale | 4 | 100.0 | 58.4 | 73.7 | 90.2 | 52.7 | 66.4 | | | | | | 0.0 | 4.3 | 3.4 | 3.3 | 4.3 3.9 | | Open | Zero-shot | 5 | 28.6 | 38.5 | 32.8 | 24.8 | 33.4 | 28.4 | | | | | | 2.0 | 3.6 | 2.3 | 2.2 | 3.4 2.4 | | | VERISCI | 6 | 52.5 | 43.8 | 47.7 | 46.9 | 39.2 | 42.6 | | | | | | 3.5 | 3.7 | 3.2 | 3.7 | 3.6 3.2 | (a)Sentence-levelresults. Abstract-level | | | | | Label-Only | | | Label+Rationale | | | --------- | --------------- | --- | ---- | ---------- | -------- | -------- | --------------- | ------------ | | Retrieval | Model | Row | P | R | F1 | P | R | F1 | | | Oraclerationale | 1 | 91.4 | 2.2 76.1 | 3.0 83.0 | 2.5 91.4 | 2.2 76.1 | 3.0 83.0 2.5 | Oracle | | Zero-shot | 2 | 88.9 | 2.8 58.3 | 3.7 70.4 | 3.2 69.2 | 3.9 45.4 | 3.5 54.8 3.5 | | --- | --------- | --- | ---- | -------- | -------- | -------- | -------- | ------------ | abstract | | VERISCI | 3 | 91.0 | 2.3 67.4 | 3.3 77.4 | 2.7 85.2 | 2.9 63.2 | 3.5 72.5 3.1 | | ---- | --------------- | --- | ---- | -------- | -------- | -------- | -------- | ------------ | | | Oraclerationale | 4 | 91.0 | 2.6 53.1 | 3.8 67.0 | 3.4 91.0 | 2.6 53.1 | 3.8 67.0 3.4 | | Open | Zero-shot | 5 | 52.7 | 41.6 | 46.5 | 43.6 | 34.4 | 38.4 | | | | | | 3.7 | 3.7 | 3.4 | 3.7 | 3.5 3.3 | | | VERISCI | 6 | 55.4 | 47.5 | 51.0 | 52.6 | 45.1 | 48.5 | | | | | | 3.7 | 3.6 | 3.3 | 3.7 | 3.6 3.3 | (b)Abstract-levelresults Table7: DevsetresultsasinTable4,reportingmeanandstandarddeviationover10,000bootstrapsamples. 7546 | Journal | | | | | Count | | Source citance | | | | | | | ------- | --- | --- | --- | --- | ----- | --- | -------------- | --- | --- | --- | --- | --- | | BMJ | | | | | 60 | | | | | | | | "Future studies are also warranted to evaluate the potential association between WNT5A/PCP | Blood | | | | | | 8 | | | | | | | | ----- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | signaling in adipose tissue and | CancerCell | | | | | | 8 | atherosclerotic CVD, given the major role that | | | | | | | ---------- | --- | --- | --- | --- | --- | --- | ----------------------------------------------- | --- | --- | --- | --- | --- | | Cell | | | | | 51 | | IL-6 signaling plays in this condition as | | | | | | revealed by large Mendelian randomization | CellMetabolism | | | | | 10 | | studies 44, 45 | | ." | | | | | -------------- | --- | --- | --- | --- | --- | --- | -------------- | --- | --- | --- | --- | --- | | CellStemCell | | | | | 41 | | | | | | | | Claim | Circulation | | | | | 12 | | | | | | | | | ----------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | IL-6 signaling plays a major role in | Immunity | | | | | 33 | | | | | | | | | -------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | atherosclerotic cardiovascular disease. | JAMA | | | | | 79 | | | | | | | | | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | MolecularCell 27 Figure 4: A claim written based on a citance. Mate- MolecularSystemsBiology 5 rialunrelatedtothecitationisremoved. Theacronym “CVD”isexpandedto“cardiovasculardisease”. | Nature | | | | | 29 | | | | | | | | | ----------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | NatureCellBiology | | | | | 26 | | | | | | | | NatureCommunications 19 ten. Theseretrievalswouldbeincorrectlymarked | NatureGenetics | | | | | | 8 | | | | | | | | -------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | wrongbyourevaluationmetrics. | NatureMedicine | | | | | 89 | | | | | | | | | -------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | Distractorabstractsasdefinedin§3.1havetwo | NatureMethods | | | | | | 1 | | | | | | | | -------------------- | --- | --- | --- | --- | --- | --- | --------- | ------- | --------- | ---------- | ------------- | ------------ | | | | | | | | | qualities | that | make them | a | good addition | to the | | NucleicAcidsResearch | | | | | 10 | | | | | | | | | | | | | | | | SCIFACT | corpus: | (1) | They | are cited | in the same | | PlosBiology | | | | | 36 | | | | | | | | | | | | | | | | articles | as our | evidence | abstracts, | | meaning that | | PlosMedicine | | | | | 38 | | | | | | | | theyoftendiscusssimilartopicsandincreasethe | Science | | | | | | 7 | difficulty | of | abstract retrieval | | methods | based on | | ---------------------------- | --- | --- | --- | --- | --- | --- | ------------------------------------------- | ---------------------------------- | -------------------------- | --- | ---------- | --------- | | ScienceTranslationalMedicine | | | | | | 2 | | | | | | | | | | | | | | | lexicalsimilarity. | | (2)Theauthorsofourcitances | | | | | TheLancet | | | | | 22 | | | | | | | | | | | | | | | | were | aware of | the distractor | | abstracts, | and chose | | Other | | | | | 120 | | nottomentiontheminthecitancesusedtogenerate | | | | | | | | | | | | | | claims. | Thismakesthemunlikelytobeasourceof | | | | | | Total | | | | | 741 | | | | | | | | falsenegativeretrievals. | Table8: | Numberofciteddocumentsbyjournal. | | | | | Some | | | | | | | | ------- | -------------------------------- | --- | --- | --- | --- | ---- | --- | ------------------ | --- | --- | --- | --- | | | | | | | | | C.2 | Annotationexamples | | | | | co-citedarticles(§3.1)comefromjournalsoutsideour curatedset;theseareindicatedby“Other”. Converting citances to claims Figure 4 shows | | | | | | | | an example | | of a citance | re-written | | as a claim. | | --- | --- | --- | --- | --- | --- | --- | ----------- | --- | ------------ | ---------- | ------------ | ----------- | | | | | | | | | The citance | | discusses | the | relationship | between | C Datasetcollectionandcorpusstatistics “atheroscleroticCVD”and“IL-6”,andcitestwopa- | C.1 Corpus | | | | | | | pers(44and45)asevidence. | | | | Toconverttoaclaim, | | | ---------- | --- | --- | --- | --- | --- | --- | ------------------------ | --- | --- | --- | ------------------ | --- | theacronym“CVD”isexpandedto“cardiovascu- | Source | journals | Table | 8 shows | | the number | of | | | | | | | | ------ | -------- | ----- | ------- | --- | ---------- | --- | --- | --- | --- | --- | --- | --- | lardisease”,irrelevantinformationisremoved,and citedabstractsfromeachofourselectedjournals. theclaimiswrittenasanatomicfactualstatement. | The “Other” | category | | includes | “co-cited” | | (§3.1) | | | | | | | | ----------- | -------- | --------- | -------- | ---------- | --------- | ------ | ------------------ | --- | --- | ---------------------- | --- | --- | | abstracts | that | came from | journals | | not among | our | | | | | | | | | | | | | | | Multiplerationales | | | Figure5showsaclaimsup- | | | pre-definedset. | | | | | | | | ported | by two | rationales | from | the same | abstract. | | --- | --- | --- | --- | --- | --- | --- | ------ | ------ | ---------- | ---- | -------- | --------- | Thetextofeachrationaleonitsownissufficientto | Distractor | abstracts | | In §3.1, | we | mention | how | | | | | | | | ----------- | ---------- | ------- | ---------- | --- | --------- | ---- | --------------- | --------------------------- | --- | --- | --- | --- | | we increase | the | size of | the corpus | | by adding | dis- | entailtheclaim. | | | | | | | tractor | abstracts. | The | reason | why | we do not | use | | | | | | | | | | | | | | | C.3 | Annotatorsandqualitycontrol | | | | | theentiretyofalargeresearchcorpuslikeS2ORC asourfact-checkingcorpusisthatdoingsowould Claimwriting Studentclaimwritersattendedan introducemanyfalsenegativeretrievals: abstracts in-person training session where they were intro- containingevidencerelevanttoagivenclaim,but ducedtothetaskandreceivedin-personfeedback notmentionedintheclaim’ssourcecitance. This fromthefourexperts. Followingtraining,student canoccureitherbecausethecitanceauthorssimply annotatorscontinuedwritingclaimsremotely. The were not aware of these abstracts, or because the expertannotatorsmonitoredclaimsforqualitydur- abstractswerepublishedafterthecitancewaswrit- ing the remote annotation process, and provided 7547 https://scifact.s3-us-west-2.amazonaws. Claim Antibiotic induced alterations in the gut com/doc/evidence-annotation-instructions. microbiome reduce resistance against pdf. Clostridium difficile | | Decision: | | SUPPORTS | | | | --- | --------- | --- | -------- | --- | --- | Antibiotics can have significant and long- lasting effects on the gastrointestinal tract microbiota, reducing colonization resistance against pathogens including Clostridium difficile. Rationale 1 Our results indicate that antibiotic-mediated alteration of the gut microbiome converts the global metabolic profile to one that favours C. difficile germination and growth. Rationale 2 Figure5:Aclaimsupportedbytworationalesfromthe | same abstract. | The | text | of each | rationale | on its own | | -------------- | --- | ---- | ------- | --------- | ---------- | providessufficientevidencetoverifytheclaim. feedbackwhennecessary;low-qualityclaimswere | returnedtotheannotatorsforre-writing. | | | | | Asafinal | | ------------------------------------- | ---------- | --- | ------ | -------------- | -------- | | check, all | submitted | | claims | were proofread | (and | | edited if | necessary) | by | an | undergraduate | whose | claimsweredeemedespeciallyhigh-qualitybythe expertannotators. | Claim negations | | As | mentioned | in §3.2, | an ex- | | --------------- | --- | --- | --------- | -------- | ------ | pertannotatorwroteclaimnegationstointroduce | caseswhereanabstractREFUTESaclaim. | | | | | Thean- | | ---------------------------------- | --- | --- | --- | --- | ------ | notatorskippedclaimsthatcouldonlybenegated | by adding | obvious | triggers | | like “not”. | The ma- | | --------- | ------- | --------- | -------- | ----------- | ----------- | | jority of | claim | negations | involved | a | reversal of | effectdirection;forinstance“Ahighmicroerythro- cytecountprotectsagainstsevereanemia”canbe negatedas“Ahighmicroerythrocytecountraises vulnerabilitytosevereanemia”. | Claimverification | | | Annotationswereperformed | | | | ----------------------------- | ------- | ------------- | ------------------------ | -------------- | ---------- | | remotelythroughawebinterface. | | | | Annotatorswere | | | required | to pass | a 10-question | | “quiz” | before an- | | notatingtheirownclaims. | | | Afterpassingthequiz, | | | subsequentsubmissionswerereviewedbyanNLP expertuntilthatexpertdeemedtheannotatorreli- able. Approvedannotatorswerethenassignedto | revieweachothers’submissions. | | | | Ingeneral,grad- | | | ----------------------------- | --- | --- | --- | --------------- | --- | uatestudentswereassignedtoreviewannotations fromundergraduates. D Annotationinterfacesandguidelines Weshowascreenshotoftheclaimwritinginterface inFigure6,andtheclaimverificationinterfacein | Figure7. | Thecompleteannotationguideforclaim | | | | | | ------------ | ---------------------------------- | --------- | --- | ------------- | ---- | | verification | is | available | at | the following | URL: | 7548 Figure 6: The claim-writing interface. The citation sentence is highlighted in blue on the top left. Additional context is provided on bottom left. The right side shows two claims that could be written based on this citation sentence. 7549 Figure7: Theevidencecollectioninterface. 7550