@inproceedings{samarinas-etal-2021-improving,
title = "Improving Evidence Retrieval for Automated Explainable Fact-Checking",
author = "Samarinas, Chris and
Hsu, Wynne and
Lee, Mong Li",
editor = "Sil, Avi and
Lin, Xi Victoria",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-demos.10",
doi = "10.18653/v1/2021.naacl-demos.10",
pages = "84--91",
abstract = "Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. We describe a three-stage automated fact-checking system, named Quin+, using evidence retrieval and selection methods. We demonstrate that using dense passage representations leads to much higher evidence recall in a noisy setting. We also propose two sentence selection approaches, an embedding-based selection using a dense retrieval model, and a sequence labeling approach for context-aware selection. Quin+ is able to verify open-domain claims using results from web search engines.",
}
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%0 Conference Proceedings
%T Improving Evidence Retrieval for Automated Explainable Fact-Checking
%A Samarinas, Chris
%A Hsu, Wynne
%A Lee, Mong Li
%Y Sil, Avi
%Y Lin, Xi Victoria
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F samarinas-etal-2021-improving
%X Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. We describe a three-stage automated fact-checking system, named Quin+, using evidence retrieval and selection methods. We demonstrate that using dense passage representations leads to much higher evidence recall in a noisy setting. We also propose two sentence selection approaches, an embedding-based selection using a dense retrieval model, and a sequence labeling approach for context-aware selection. Quin+ is able to verify open-domain claims using results from web search engines.
%R 10.18653/v1/2021.naacl-demos.10
%U https://aclanthology.org/2021.naacl-demos.10
%U https://doi.org/10.18653/v1/2021.naacl-demos.10
%P 84-91
Markdown (Informal)
[Improving Evidence Retrieval for Automated Explainable Fact-Checking](https://aclanthology.org/2021.naacl-demos.10) (Samarinas et al., NAACL 2021)
ACL
- Chris Samarinas, Wynne Hsu, and Mong Li Lee. 2021. Improving Evidence Retrieval for Automated Explainable Fact-Checking. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations, pages 84–91, Online. Association for Computational Linguistics.