Abstract
This paper contains our system description for the second Fact Extraction and VERification (FEVER) challenge. We propose a two-staged sentence selection strategy to account for examples in the dataset where evidence is not only conditioned on the claim, but also on previously retrieved evidence. We use a publicly available document retrieval module and have fine-tuned BERT checkpoints for sentence se- lection and as the entailment classifier. We report a FEVER score of 68.46% on the blind testset.- Anthology ID:
- D19-6616
- Volume:
- Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 105–109
- Language:
- URL:
- https://aclanthology.org/D19-6616
- DOI:
- 10.18653/v1/D19-6616
- Cite (ACL):
- Dominik Stammbach and Guenter Neumann. 2019. Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task. In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 105–109, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task (Stammbach & Neumann, 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D19-6616.pdf