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        <title>“Who said it, and Why?” Provenance for Natural Language Claims</title>
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        <namePart type="given">Yi</namePart>
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        <namePart type="family">Ives</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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            <namePart type="given">Natalie</namePart>
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    <abstract>In an era where generating content and publishing it is so easy, we are bombarded with information and are exposed to all kinds of claims, some of which do not always rank high on the truth scale. This paper suggests that the key to a longer-term, holistic, and systematic approach to navigating this information pollution is capturing the provenance of claims. To do that, we develop a formal definition of provenance graph for a given natural language claim, aiming to understand where the claim may come from and how it has evolved. To construct the graph, we model provenance inference, formulated mainly as an information extraction task and addressed via a textual entailment model. We evaluate our approach using two benchmark datasets, showing initial success in capturing the notion of provenance and its effectiveness on the application of claim verification.</abstract>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T “Who said it, and Why?” Provenance for Natural Language Claims
%A Zhang, Yi
%A Ives, Zachary
%A Roth, Dan
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-said
%X In an era where generating content and publishing it is so easy, we are bombarded with information and are exposed to all kinds of claims, some of which do not always rank high on the truth scale. This paper suggests that the key to a longer-term, holistic, and systematic approach to navigating this information pollution is capturing the provenance of claims. To do that, we develop a formal definition of provenance graph for a given natural language claim, aiming to understand where the claim may come from and how it has evolved. To construct the graph, we model provenance inference, formulated mainly as an information extraction task and addressed via a textual entailment model. We evaluate our approach using two benchmark datasets, showing initial success in capturing the notion of provenance and its effectiveness on the application of claim verification.
%R 10.18653/v1/2020.acl-main.406
%U https://aclanthology.org/2020.acl-main.406
%U https://doi.org/10.18653/v1/2020.acl-main.406
%P 4416-4426

Markdown (Informal)

[“Who said it, and Why?” Provenance for Natural Language Claims](https://aclanthology.org/2020.acl-main.406) (Zhang et al., ACL 2020)

• “Who said it, and Why?” Provenance for Natural Language Claims (Zhang et al., ACL 2020)

ACL

• Yi Zhang, Zachary Ives, and Dan Roth. 2020. “Who said it, and Why?” Provenance for Natural Language Claims. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4416–4426, Online. Association for Computational Linguistics.