@inproceedings{zhang-etal-2021-article,
title = "What is Your Article Based On? Inferring Fine-grained Provenance",
author = "Zhang, Yi and
Ives, Zachary and
Roth, Dan",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.458",
doi = "10.18653/v1/2021.acl-long.458",
pages = "5894--5903",
abstract = "When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study of \textit{claim provenance}, which seeks to trace and explain the origins of claims. In this paper, we introduce new techniques to model and reason about the provenance of \textit{multiple} interacting claims, including how to capture \textit{fine-grained} information about the context. Our solution hinges on first identifying the sentences that potentially contain important external information. We then develop a query generator with our novel \textit{rank-aware cross attention} mechanism, which aims at generating metadata for the source article, based on the context and the signals collected from a search engine. This establishes relevant search queries, and it allows us to obtain source article candidates for each identified sentence and propose an ILP based algorithm to infer the best sources. We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from \url{www.politifact.com}; our experimental results show that our solution leads to a significant improvement over baselines.",
}
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<abstract>When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study of claim provenance, which seeks to trace and explain the origins of claims. In this paper, we introduce new techniques to model and reason about the provenance of multiple interacting claims, including how to capture fine-grained information about the context. Our solution hinges on first identifying the sentences that potentially contain important external information. We then develop a query generator with our novel rank-aware cross attention mechanism, which aims at generating metadata for the source article, based on the context and the signals collected from a search engine. This establishes relevant search queries, and it allows us to obtain source article candidates for each identified sentence and propose an ILP based algorithm to infer the best sources. We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from www.politifact.com; our experimental results show that our solution leads to a significant improvement over baselines.</abstract>
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%0 Conference Proceedings
%T What is Your Article Based On? Inferring Fine-grained Provenance
%A Zhang, Yi
%A Ives, Zachary
%A Roth, Dan
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2021-article
%X When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study of claim provenance, which seeks to trace and explain the origins of claims. In this paper, we introduce new techniques to model and reason about the provenance of multiple interacting claims, including how to capture fine-grained information about the context. Our solution hinges on first identifying the sentences that potentially contain important external information. We then develop a query generator with our novel rank-aware cross attention mechanism, which aims at generating metadata for the source article, based on the context and the signals collected from a search engine. This establishes relevant search queries, and it allows us to obtain source article candidates for each identified sentence and propose an ILP based algorithm to infer the best sources. We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from www.politifact.com; our experimental results show that our solution leads to a significant improvement over baselines.
%R 10.18653/v1/2021.acl-long.458
%U https://aclanthology.org/2021.acl-long.458
%U https://doi.org/10.18653/v1/2021.acl-long.458
%P 5894-5903
Markdown (Informal)
[What is Your Article Based On? Inferring Fine-grained Provenance](https://aclanthology.org/2021.acl-long.458) (Zhang et al., ACL-IJCNLP 2021)
ACL
- Yi Zhang, Zachary Ives, and Dan Roth. 2021. What is Your Article Based On? Inferring Fine-grained Provenance. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5894–5903, Online. Association for Computational Linguistics.