Abstract
Accurate fact verification depends on performing fine-grained reasoning over crucial entities by capturing their latent logical relations hidden in multiple evidence clues, which is generally lacking in existing fact verification models. In this work, we propose a novel Global-to-Local Aggregation and Fission network (GLAF) to fill this gap. Instead of treating entire sentences or all semantic elements within them as nodes to construct a coarse-grained or unstructured evidence graph as in previous methods, GLAF constructs a fine-grained and structured evidence graph by parsing the rambling sentences into structural triple-level reasoning clues and regarding them as graph nodes to achieve fine-grained and interpretable evidence graph reasoning. Specifically, to capture latent logical relations between the clues, GLAF first employs a local fission reasoning layer to conduct fine-grained multi-hop reasoning, and then uses a global evidence aggregation layer to achieve information sharing and the interchange of evidence clues for final claim label prediction. Experimental results on the FEVER dataset demonstrate the effectiveness of GLAF, showing that it achieves the state-of-the-art performance by obtaining a 77.62% FEVER score.- Anthology ID:
- 2022.coling-1.155
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1801–1812
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.155
- DOI:
- Cite (ACL):
- Zhiyuan Ma, Jianjun Li, Guohui Li, and Yongjing Cheng. 2022. GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1801–1812, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification (Ma et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2022.coling-1.155.pdf
- Data
- FEVER