QED: A fact verification system for the FEVER shared task

Jackson Luken, Nanjiang Jiang, Marie-Catherine de Marneffe


Abstract
This paper describes our system submission to the 2018 Fact Extraction and VERification (FEVER) shared task. The system uses a heuristics-based approach for evidence extraction and a modified version of the inference model by Parikh et al. (2016) for classification. Our process is broken down into three modules: potentially relevant documents are gathered based on key phrases in the claim, then any possible evidence sentences inside those documents are extracted, and finally our classifier discards any evidence deemed irrelevant and uses the remaining to classify the claim’s veracity. Our system beats the shared task baseline by 12% and is successful at finding correct evidence (evidence retrieval F1 of 62.5% on the development set).
Anthology ID:
W18-5526
Volume:
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
156–160
Language:
URL:
https://aclanthology.org/W18-5526
DOI:
10.18653/v1/W18-5526
Bibkey:
Cite (ACL):
Jackson Luken, Nanjiang Jiang, and Marie-Catherine de Marneffe. 2018. QED: A fact verification system for the FEVER shared task. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 156–160, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
QED: A fact verification system for the FEVER shared task (Luken et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5526.pdf
Data
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