@inproceedings{staliunaite-vlachos-2025-dis2dis,
title = "{D}is2{D}is: Explaining Ambiguity in Fact-Checking",
author = "Staliunaite, Ieva and
Vlachos, Andreas",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.14/",
doi = "10.18653/v1/2025.findings-naacl.14",
pages = "246--267",
ISBN = "979-8-89176-195-7",
abstract = "Ambiguity is a linguistic tool for encoding information efficiently, yet it also causes misunderstandings and disagreements. It is particularly relevant to the domain of misinformation, as fact-checking ambiguous claims is difficult even for experts. In this paper we argue that instead of predicting a veracity label for which there is genuine disagreement, it would be more beneficial to explain the ambiguity. Thus, this work introduces claim disambiguation, a constrained generation task, for explaining ambiguous claims in fact-checking. This involves editing them to spell out an interpretation that can then be unequivocally supported by the given evidence. We collect a dataset of 1501 such claim revisions and conduct experiments with sequence-to-sequence models. The performance is compared to a simple copy baseline and a Large Language Model baseline. The best results are achieved by employing Minimum Bayes Decoding, with a BertScore F1 of 92.22. According to human evaluation, the model successfully disambiguates the claims 72{\%} of the time."
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%0 Conference Proceedings
%T Dis2Dis: Explaining Ambiguity in Fact-Checking
%A Staliunaite, Ieva
%A Vlachos, Andreas
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F staliunaite-vlachos-2025-dis2dis
%X Ambiguity is a linguistic tool for encoding information efficiently, yet it also causes misunderstandings and disagreements. It is particularly relevant to the domain of misinformation, as fact-checking ambiguous claims is difficult even for experts. In this paper we argue that instead of predicting a veracity label for which there is genuine disagreement, it would be more beneficial to explain the ambiguity. Thus, this work introduces claim disambiguation, a constrained generation task, for explaining ambiguous claims in fact-checking. This involves editing them to spell out an interpretation that can then be unequivocally supported by the given evidence. We collect a dataset of 1501 such claim revisions and conduct experiments with sequence-to-sequence models. The performance is compared to a simple copy baseline and a Large Language Model baseline. The best results are achieved by employing Minimum Bayes Decoding, with a BertScore F1 of 92.22. According to human evaluation, the model successfully disambiguates the claims 72% of the time.
%R 10.18653/v1/2025.findings-naacl.14
%U https://aclanthology.org/2025.findings-naacl.14/
%U https://doi.org/10.18653/v1/2025.findings-naacl.14
%P 246-267
Markdown (Informal)
[Dis2Dis: Explaining Ambiguity in Fact-Checking](https://aclanthology.org/2025.findings-naacl.14/) (Staliunaite & Vlachos, Findings 2025)
ACL
- Ieva Staliunaite and Andreas Vlachos. 2025. Dis2Dis: Explaining Ambiguity in Fact-Checking. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 246–267, Albuquerque, New Mexico. Association for Computational Linguistics.