The Fact Extraction and VERification (FEVER) Shared Task

James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal


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.
Anthology ID:
W18-5501
Original:
W18-5501v1
Version 2:
W18-5501v2
Version 3:
W18-5501v3
Volume:
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2018
Address:
Brussels, Belgium
Editors:
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–9
Language:
URL:
https://aclanthology.org/W18-5501
DOI:
10.18653/v1/W18-5501
Bibkey:
Cite (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.
Cite (Informal):
The Fact Extraction and VERification (FEVER) Shared Task (Thorne et al., EMNLP 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ml4al-ingestion/W18-5501.pdf
Data
FEVER