@inproceedings{saeed-etal-2021-neural,
title = "Neural Re-rankers for Evidence Retrieval in the {FEVEROUS} Task",
author = "Saeed, Mohammed and
Alfarano, Giulio and
Nguyen, Khai and
Pham, Duc and
Troncy, Raphael and
Papotti, Paolo",
editor = "Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)",
month = nov,
year = "2021",
address = "Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.fever-1.12",
doi = "10.18653/v1/2021.fever-1.12",
pages = "108--112",
abstract = "Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities. A plethora of solutions have been developed, but methods which leverage both structured and unstructured information to detect misinformation are of particular relevance. In this paper, we tackle the FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) challenge which consists of an open source baseline system together with a benchmark dataset containing 87,026 verified claims. We extend this baseline model by improving the evidence retrieval module yielding the best evidence F1 score among the competitors in the challenge leaderboard while obtaining an overall FEVEROUS score of 0.20 (5th best ranked system).",
}
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<abstract>Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities. A plethora of solutions have been developed, but methods which leverage both structured and unstructured information to detect misinformation are of particular relevance. In this paper, we tackle the FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) challenge which consists of an open source baseline system together with a benchmark dataset containing 87,026 verified claims. We extend this baseline model by improving the evidence retrieval module yielding the best evidence F1 score among the competitors in the challenge leaderboard while obtaining an overall FEVEROUS score of 0.20 (5th best ranked system).</abstract>
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%0 Conference Proceedings
%T Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task
%A Saeed, Mohammed
%A Alfarano, Giulio
%A Nguyen, Khai
%A Pham, Duc
%A Troncy, Raphael
%A Papotti, Paolo
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Dominican Republic
%F saeed-etal-2021-neural
%X Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities. A plethora of solutions have been developed, but methods which leverage both structured and unstructured information to detect misinformation are of particular relevance. In this paper, we tackle the FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) challenge which consists of an open source baseline system together with a benchmark dataset containing 87,026 verified claims. We extend this baseline model by improving the evidence retrieval module yielding the best evidence F1 score among the competitors in the challenge leaderboard while obtaining an overall FEVEROUS score of 0.20 (5th best ranked system).
%R 10.18653/v1/2021.fever-1.12
%U https://aclanthology.org/2021.fever-1.12
%U https://doi.org/10.18653/v1/2021.fever-1.12
%P 108-112
Markdown (Informal)
[Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task](https://aclanthology.org/2021.fever-1.12) (Saeed et al., FEVER 2021)
ACL
- Mohammed Saeed, Giulio Alfarano, Khai Nguyen, Duc Pham, Raphael Troncy, and Paolo Papotti. 2021. Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task. In Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER), pages 108–112, Dominican Republic. Association for Computational Linguistics.