@inproceedings{thorne-etal-2018-fact,
title = "The Fact Extraction and {VER}ification ({FEVER}) Shared Task",
author = "Thorne, James and
Vlachos, Andreas and
Cocarascu, Oana and
Christodoulopoulos, Christos and
Mittal, Arpit",
editor = "Thorne, James and
Vlachos, Andreas and
Cocarascu, Oana and
Christodoulopoulos, Christos and
Mittal, Arpit",
booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5501/",
doi = "10.18653/v1/W18-5501",
pages = "1--9",
abstract = "We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia. We received entries from 23 competing teams, 19 of which scored higher than the previously published baseline. The best performing system achieved a FEVER score of 64.21{\%}. In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems."
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%0 Conference Proceedings
%T The Fact Extraction and VERification (FEVER) Shared Task
%A Thorne, James
%A Vlachos, Andreas
%A Cocarascu, Oana
%A Christodoulopoulos, Christos
%A Mittal, Arpit
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F thorne-etal-2018-fact
%X We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia. We received entries from 23 competing teams, 19 of which scored higher than the previously published baseline. The best performing system achieved a FEVER score of 64.21%. In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.
%R 10.18653/v1/W18-5501
%U https://aclanthology.org/W18-5501/
%U https://doi.org/10.18653/v1/W18-5501
%P 1-9
Markdown (Informal)
[The Fact Extraction and VERification (FEVER) Shared Task](https://aclanthology.org/W18-5501/) (Thorne et al., EMNLP 2018)
ACL
- James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2018. The Fact Extraction and VERification (FEVER) Shared Task. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 1–9, Brussels, Belgium. Association for Computational Linguistics.