Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging

Nayeon Lee, Chien-Sheng Wu, Pascale Fung


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
Bibkey:
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)
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PDF:
https://aclanthology.org/D18-1143.pdf