A mbi FC : Fact-Checking Ambiguous Claims with Evidence (2024)

Abstract: Automated fact-checking systems verify claims against evidence to predict their veracity. In real-world scenarios, the retrieved evidence may not unambiguously support or refute the claim and yield conflicting but valid interpretations. Existing fact-checking datasets assume that the models developed with them predict a single veracity label for each claim, thus discouraging the handling of such ambiguity. To address this issue we present AmbiFC,1 a fact-checking dataset with 10k claims derived from real-world information needs. It contains fine-grained evidence annotations of 50k passages from 5k Wikipedia pages. We analyze the disagreements arising from ambiguity when comparing claims against evidence in AmbiFC, observing a strong correlation of annotator disagreement with linguistic phenomena such as underspecification and probabilistic reasoning. We develop models for predicting veracity handling this ambiguity via soft labels, and find that a pipeline that learns the label distribution for sentence-level evidence selection and veracity prediction yields the best performance. We compare models trained on different subsets of AmbiFC and show that models trained on the ambiguous instances perform better when faced with the identified linguistic phenomena.

## introduction in natural language processing, the task of automated fact-checking is given a claim of unknown veracity, to identify evidence from a corpus of documents, and predict whether the evidence sup-ports or refutes the claim. it has received considerable attention in recent years @xcite and gained renewed relevance due to the hallucination of unsupported or even false statements in natural language generation tasks, including information-seeking dialogues @xcite . automated fact-checking is closely related to natural language inference (nli) where the evidence is considered given @xcite @xcite . several studies @xcite @xcite have shown that nli suffers from inherent ambiguity leading to conflicting yet valid annotations. to address this, recent work has focused on utilizing these conflicting annotations, especially when aggregated labels are not considered to adequately represent the task @xcite . many fact-checking datasets are purpose-made rather than naturally occurring, similar to those used in nli; their claims are often created by manipulating sentences from the evidence documents @xcite @xcite . as a result, they are unlikely to represent real-world information needs, as they are written with knowledge of the evidence. on the other hand, in datasets with real-world claims evidence is often used without manual annotation, assuming that it is sufficient @xcite . if evidence annotation is performed, datasets include artificially created incorrect claims, ensuring that the used evidence contradicts the claims @xcite , or exhibits low annotator agreement @xcite without figure 1 : an example of an instance of claim and wikipedia passage which is ambiguous due to underspecification. we consider all supporting (s), refuting (r), and neutral (n) annotations as valid perspectives. given a claim and a wikipedia passage, the model must predict soft labels derived from these annotations. more concretely, the claim that ''it is illegal in illinois to record a conversation'' in figure 1 seems clear on its own, yet becomes ambiguous when compared to the evidence, as it is underspecified. the claim does not explicitly state whether the recording was done surreptitiously (i.e., secretively), allowing for various interpretations: (a) as refuting the claim since it is legal if not done surreptitiously, and (b) as neutral as it is impossible to determine whether it refutes or supports the claim without information about the recording intent. surreptitious recording only pertains to a specific case and none of the annotators deemed it as prominent enough to provide overall support for the claim. in this study we aim to investigate the presence of such ambiguities in fact-checking using realistic claims and evidence. to this end, we present ambifc, a large fact-checking dataset derived from real-world information needs, sourced from real-world yes/no questions of boolq @xcite . ambifc contains evidence annotations at the passage and sentence level from full wikipedia pages, from a minimum of five annotations per instance. unlike previous fact-checking datasets we consider each annotation as a valid perspective of the claim's veracity given a wikipedia passage as evidence, and task models to predict the veracity via soft labels that consider all annotations. we provide explanations for the annotator disagreement via our annotations of linguistic phenomena, inspired by @xcite , adding inference types id-iosyncratic to fact-checking. further, we experiment with three established methods to model annotator disagreement. our work emphasizes the importance of ambiguity within automated fact-checking and takes a step towards incorporating ambiguity into fact-checking models. in automated fact-checking, earlier works constructed complex claims from question answering datasets @xcite @xcite or knowledge graphs @xcite . our work is most comparable to faviq @xcite , which was also generated from real-world information needs questions. unlike ambifc, it lacks evidence annotations and utilizes disambiguated question-answer pairs from ambiqa @xcite , hence excluding the natural ambiguity of claims based on real-world information needs, studied in this work. other works gathered claims from credible sources such as scientific publications or wikipedia, using cited documents as evidence. this provides realistic claims which are only supported by evidence, and requires the generation of artificial refuted claims @xcite @xcite , or only distinguishes between different levels of support @xcite . another line of research collects claims from professional fact-checking organizations. these works often face disagreement among annotators but do not handle ambiguity @xcite , or do not provide annotated evidence @xcite . the recently published averitec dataset @xcite reconstructs the fact-checkers' reasoning via questions and answers from evidence documents. in ambifc we consider claims that are interesting according to the search queries used in constructing boolq @xcite , not claims deemed check-worthy by fact-checkers. additionally, we provide passage-and sentencelevel annotation, and address uncertainty and disagreement. in the domain of nli, nie et al. (2020, chaos-nli) presented a comprehensive annotation of nli items, involving 100 annotators for each item. jiang and marneffe (2022) further investigate the causes of disagreement in chaosnli, categorizing them into pragmatic and lexical features, as well as general patterns of human behavior under annotation instructions. our work extends the existing work in nli to fact-checking, by examining the types of linguistic phenomena common in the two tasks. @xcite and @xcite provide overviews of the current state of modeling and evaluation techniques for data with annotation variance. they highlight various methods, such as calibration, sequential fine-tuning, repeated labeling, learning from soft labels, and variants of multi-task learning. ## preliminaries each instance (c, p ) comprises a claim c and a passage p from wikipedia. a passage @xmath0, s 2 , . . . , s n ] is composed of n sentences s i . annotations are collected for the entire passage p and for each individual s @xmath1, indicating their stance towards c as supporting, refuting, or neutral. these ternary sentence-level annotations expressing stance towards the claim can be mapped to binary annotations by treating non-neutral annotations as ''evidence'' regardless of stance. we do not aggregate passage-level annotations into hard veracity labels. instead, for each (c, p ) we use soft labels, representing the veracity as a distribution of the passage-level annotations given a claim. we specifically focus on the fact-checking subtasks of evidence selection (ev.) and veracity prediction (ver.) for each claim-passage instance (c p ). we consider each sentence s i as part of the evidence e for c if at least one non-neutral annotation for it exists. for the evidence selection subtask, the model must select all evidence sentences s @xmath2. in the veracity prediction subtask, the model must predict the veracity of c given p using soft labels that represent the annotation distribution at the passage level (figure 1 ). in addition to comparing the predicted and human label distributions, we assess the models using less stringent metrics (outlined in §6.2.2) to accommodate potential annotation noise. ## the ambifc dataset to create the claims and annotate them with evidence, we followed a two-step process. first, crowd-workers transformed questions from boolq into assertive statements. second, the crowdworkers labeled evidence sentences and passages from a wikipedia page to indicate whether they support or refute the corresponding claim. claims boolq comprises knowledge-seeking user queries with yes-or-no answers, similar to fact-checking intentions. dataset instances are generated by rephrasing these queries into claims. two annotators on mechanical turk rephrase each boolq question as a claim, with instructions to retain as many tokens from the original question as possible. in case the claims by the two annotators were different, they were included in the dataset after manual review. the crowd-workers underwent a qualification round evaluated by the authors. a total of 512 unique annotators with a 95% acceptance rate completed the task; 20% of hits were used for worker qualification and training, 80% form the final dataset. evidence annotation for each claim, the full wikipedia page from boolq containing the answer to the yes/no question was used as evidence. to prevent positional bias, where annotators concentrate on a page's beginning, and annotator fatigue, pages were divided into multiple passagelevel annotation tasks (capped at 20 contiguous sentences). annotators assessed each sentence in a passage as supporting, refuting, or neutral towards the claim, and provided an overall judgment of the claim's veracity given the passage. passages without evidence sentences were labeled neutral. in anticipation of potentially low inter-annotator agreement as observed in comparable annotation tasks @xcite , we introduce a second level of passage annotation to indicate uncertainty: if annotators chose ''neutral'' they could additionally flag passages as ''relevant'' to differentiate it from entirely unrelated passages. nonneutral passage annotations could be flagged as ''uncertain'' by the annotators. we treat both of these additional labels (''relevant'' for ''neutral'' instances and ''uncertain'' for non-neutral ones) as indicators of unclear decision boundaries. passages received two initial annotations, with an additional three for passages with at least one supporting or refuting initial annotation, resulting in five annotations per instance in these cases. instances with identical claims (from identical paraphrasing of questions by different annotators) and passages were merged, resulting in instances with more than five annotations. quality controls annotators underwent a 3-stage approval process consisting of a qualification quiz, manual review of their first 100 hits and continuous manual review. errors were communicated to them to provide formative feedback. a batch of claims was sampled daily for continuous manual review during annotation. the authors reviewed and accepted 12,137 hits (5.2% of all annotation tasks), while corrections were provided for additional 400 hits, indicating a 3.2% error rate where annotators deviated from guidelines, not due to differences in opinion. the number of hits reviewed for each annotator was proportional to the annotator's error rate and the number of annotations submitted. annotation times were used to calibrate worker hourly pay at $22. agreement the inter-annotator agreement in terms of krippendorff's α on the collected data is 0.488 on the passage veracity labels and 0.394 on the sentence level. the disagreement implies that single labels cannot capture all valid viewpoints, necessitating the use of soft labels for evaluation. fully neutral samples have only two annotations (as per dataset construction), which is insufficient for reliable evaluation of soft labels, unless we can ensure that they are indeed 100% neutral. we estimate the probability of misclassifying an instance as fully neutral when only seeing two annotations, by randomly selecting two annotations from samples with 5+ annotations. the likelihood of wrongly assuming an instance as fully neutral when observing two neutral annotations is 0.9% for the entire dataset but it increases up to 20.9% when sampling from uncertain instances. using this estimate, we omit fully neutral (but ''relevant'') instances from our experiments, while retaining them in our linguistic analysis in §5.2. ## analysis errors by linguistic category model performance varies depending on which lexical, pragmatic, and discourse inference types are present in the items. we compare the predictions of the best performing model (annotation distillation, last row in furthermore, the largest difference appears in the subsets of the development set which contain underspecification, vagueness, probabilistic enrichment, and coreference, and the first three of these categories have the strongest correlation with annotator disagreement, as seen in ## conclusions we present ambifc, a fact-checking dataset with annotations for evidence-based fact-checking, addressing the inherent ambiguity in real-world scenarios. we find that annotator disagreement signals ambiguity rather than noise and provide explanations for this phenomenon through an analysis of linguistic phenomena. we establish baselines for fact-checking ambiguous claims, leaving room for improvement, particularly in the area of evidence selection. by publishing ambifc along with its annotations, we aim to contribute to research integrating annotations into trained models. limitations claims in ambifc are based on real-world information needs. they are not collected from real-world sources and differ from claims seen as check-worthy by human factcheckers. ambifc lacks evidence retrieval beyond the passage level. it contains different veracity labels for the same claim given different passages, without overall verdict. models trained on ambifc are constrained to this domain and only address partial aspects of complete fact-checking applications, as defined by @xcite . max glockner is supported by the german federal ministry of education and research and the hessian ministry of higher education, research, science and the arts within their joint support of the national research center for applied cybersecurity athene. ieva staliūnaitė is supported by huawei. james thorne is supported by institute of information & communications technology planning & evaluation (iitp) grant funded by the korea government (msit) (no.2019-0-00075, artificial intelligence graduate school program [kaist]). gisela vallejo is supported by the graduate research scholarship from the faculty of engineering and information technology, university of melbourne. andreas vlachos is supported by the erc grant averitec (ga 865958) and the eu h2020 grant monitio (ga 965576).