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        <title>Time Matters: An End-to-End Solution for Temporal Claim Verification</title>
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    <abstract>Automated claim verification plays an essential role in fostering trust in the digital space. Despite the growing interest, the verification of temporal claims has not received much attention in the community. Temporal claim verification brings new challenges where cues of the temporal information need to be extracted, and temporal reasoning involving various temporal aspects of the text must be applied.In this work, we describe an end-to-end solution for temporal claim verification that considers the temporal information in claims to obtain relevant evidence sentences and harnesses the power of a large language model for temporal reasoning. We curate two datasets comprising a diverse range of temporal claims to learn time-sensitive representations that encapsulate not only the semantic relationships among the events, but also their chronological proximity.Experiment results demonstrate that the proposed approach significantly enhances the accuracy of temporal claim verification, thereby advancing current state-of-the-art in automated claim verification.</abstract>
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%0 Conference Proceedings
%T Time Matters: An End-to-End Solution for Temporal Claim Verification
%A Barik, Anab Maulana
%A Hsu, Wynne
%A Lee, Mong-Li
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F barik-etal-2024-time
%X Automated claim verification plays an essential role in fostering trust in the digital space. Despite the growing interest, the verification of temporal claims has not received much attention in the community. Temporal claim verification brings new challenges where cues of the temporal information need to be extracted, and temporal reasoning involving various temporal aspects of the text must be applied.In this work, we describe an end-to-end solution for temporal claim verification that considers the temporal information in claims to obtain relevant evidence sentences and harnesses the power of a large language model for temporal reasoning. We curate two datasets comprising a diverse range of temporal claims to learn time-sensitive representations that encapsulate not only the semantic relationships among the events, but also their chronological proximity.Experiment results demonstrate that the proposed approach significantly enhances the accuracy of temporal claim verification, thereby advancing current state-of-the-art in automated claim verification.
%U https://aclanthology.org/2024.emnlp-industry.48
%P 657-664

Markdown (Informal)

[Time Matters: An End-to-End Solution for Temporal Claim Verification](https://aclanthology.org/2024.emnlp-industry.48) (Barik et al., EMNLP 2024)

• Time Matters: An End-to-End Solution for Temporal Claim Verification (Barik et al., EMNLP 2024)

ACL

• Anab Maulana Barik, Wynne Hsu, and Mong-Li Lee. 2024. Time Matters: An End-to-End Solution for Temporal Claim Verification. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 657–664, Miami, Florida, US. Association for Computational Linguistics.