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AmbiFC: Fact-Checking Ambiguous Claims with Evidence

Glockner, Max ; Staliūnaitė, Ieva ; Thorne, James ; Vallejo, Gisela ; Vlachos, Andreas ; Gurevych, Iryna (2024)
AmbiFC: Fact-Checking Ambiguous Claims with Evidence.
In: Transactions of the Association for Computational Linguistics, 12
doi: 10.1162/tacl_a_00629
Article, Bibliographie

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.

Item Type: Article
Erschienen: 2024
Creators: Glockner, Max ; Staliūnaitė, Ieva ; Thorne, James ; Vallejo, Gisela ; Vlachos, Andreas ; Gurevych, Iryna
Type of entry: Bibliographie
Title: AmbiFC: Fact-Checking Ambiguous Claims with Evidence
Language: English
Date: 2024
Publisher: MIT Press
Journal or Publication Title: Transactions of the Association for Computational Linguistics
Volume of the journal: 12
DOI: 10.1162/tacl_a_00629
URL / URN: https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00629...
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.

Uncontrolled Keywords: UKP_p_seditrah_factcheck
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 18 Jan 2024 14:21
Last Modified: 18 Jan 2024 14:21
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