Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks

Christopher Hidey, Mona Diab


Abstract
Many tasks such as question answering and reading comprehension rely on information extracted from unreliable sources. These systems would thus benefit from knowing whether a statement from an unreliable source is correct. We present experiments on the FEVER (Fact Extraction and VERification) task, a shared task that involves selecting sentences from Wikipedia and predicting whether a claim is supported by those sentences, refuted, or there is not enough information. Fact checking is a task that benefits from not only asserting or disputing the veracity of a claim but also finding evidence for that position. As these tasks are dependent on each other, an ideal model would consider the veracity of the claim when finding evidence and also find only the evidence that is relevant. We thus jointly model sentence extraction and verification on the FEVER shared task. Among all participants, we ranked 5th on the blind test set (prior to any additional human evaluation of the evidence).
Anthology ID:
W18-5525
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:
150–155
Language:
URL:
https://aclanthology.org/W18-5525
DOI:
10.18653/v1/W18-5525
Bibkey:
Cite (ACL):
Christopher Hidey and Mona Diab. 2018. Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 150–155, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks (Hidey & Diab, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5525.pdf
Data
FEVER