@inproceedings{dehaven-scott-2023-bevers,
title = "{BEVERS}: A General, Simple, and Performant Framework for Automatic Fact Verification",
author = "DeHaven, Mitchell and
Scott, Stephen",
editor = "Akhtar, Mubashara and
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 Sixth Fact Extraction and VERification Workshop (FEVER)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.fever-1.6",
doi = "10.18653/v1/2023.fever-1.6",
pages = "58--65",
abstract = "Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final claim classification, however, we spend considerable effort ensuring optimal performance for each component. The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished. We also apply this pipeline to another fact verification dataset, Scifact, and achieve the highest label accuracy among all systems on that dataset as well. We also make our full code available.",
}
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<abstract>Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final claim classification, however, we spend considerable effort ensuring optimal performance for each component. The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished. We also apply this pipeline to another fact verification dataset, Scifact, and achieve the highest label accuracy among all systems on that dataset as well. We also make our full code available.</abstract>
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%0 Conference Proceedings
%T BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification
%A DeHaven, Mitchell
%A Scott, Stephen
%Y Akhtar, Mubashara
%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 Sixth Fact Extraction and VERification Workshop (FEVER)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F dehaven-scott-2023-bevers
%X Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final claim classification, however, we spend considerable effort ensuring optimal performance for each component. The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished. We also apply this pipeline to another fact verification dataset, Scifact, and achieve the highest label accuracy among all systems on that dataset as well. We also make our full code available.
%R 10.18653/v1/2023.fever-1.6
%U https://aclanthology.org/2023.fever-1.6
%U https://doi.org/10.18653/v1/2023.fever-1.6
%P 58-65
Markdown (Informal)
[BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification](https://aclanthology.org/2023.fever-1.6) (DeHaven & Scott, FEVER 2023)
ACL