Abstract
Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this problem. We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80%, with (x65) speedup compared to a TF-IDF method. Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable. As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup.- Anthology ID:
- D18-1143
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1133–1138
- Language:
- URL:
- https://aclanthology.org/D18-1143
- DOI:
- 10.18653/v1/D18-1143
- Cite (ACL):
- Nayeon Lee, Chien-Sheng Wu, and Pascale Fung. 2018. Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1133–1138, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging (Lee et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D18-1143.pdf