Abstract
This paper presents the ColumbiaNLP submission for the FEVER Workshop Shared Task. Our system is an end-to-end pipeline that extracts factual evidence from Wikipedia and infers a decision about the truthfulness of the claim based on the extracted evidence. Our pipeline achieves significant improvement over the baseline for all the components (Document Retrieval, Sentence Selection and Textual Entailment) both on the development set and the test set. Our team finished 6th out of 24 teams on the leader-board based on the preliminary results with a FEVER score of 49.06 on the blind test set compared to 27.45 of the baseline system.- Anthology ID:
- W18-5521
- 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:
- 127–131
- Language:
- URL:
- https://aclanthology.org/W18-5521
- DOI:
- 10.18653/v1/W18-5521
- Cite (ACL):
- Tuhin Chakrabarty, Tariq Alhindi, and Smaranda Muresan. 2018. Robust Document Retrieval and Individual Evidence Modeling for Fact Extraction and Verification.. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 127–131, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Robust Document Retrieval and Individual Evidence Modeling for Fact Extraction and Verification. (Chakrabarty et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/W18-5521.pdf
- Code
- tuhinjubcse/FEVER-EMNLP