A DQN-based Approach to Finding Precise Evidences for Fact Verification
Hai Wan, Haicheng Chen, Jianfeng Du, Weilin Luo, Rongzhen Ye
Abstract
Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV. Despite being important, precise evidences are rarely studied by existing methods for FV. It is challenging to find precise evidences due to a large search space with lots of local optimums. Inspired by the strong exploration ability of the deep Q-learning network (DQN), we propose a DQN-based approach to retrieval of precise evidences. In addition, to tackle the label bias on Q-values computed by DQN, we design a post-processing strategy which seeks best thresholds for determining the true labels of computed evidences. Experimental results confirm the effectiveness of DQN in computing precise evidences and demonstrate improvements in achieving accurate claim verification.- Anthology ID:
- 2021.acl-long.83
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1030–1039
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.83
- DOI:
- 10.18653/v1/2021.acl-long.83
- Cite (ACL):
- Hai Wan, Haicheng Chen, Jianfeng Du, Weilin Luo, and Rongzhen Ye. 2021. A DQN-based Approach to Finding Precise Evidences for Fact Verification. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1030–1039, Online. Association for Computational Linguistics.
- Cite (Informal):
- A DQN-based Approach to Finding Precise Evidences for Fact Verification (Wan et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.acl-long.83.pdf
- Code
- sysulic/dqn-fv
- Data
- FEVER