Abstract
Document-level event argument extraction aims to identify event arguments beyond sentence level, where a significant challenge is to model long-range dependencies. Focusing on this challenge, we present a new chain reasoning paradigm for the task, which can generate decomposable first-order logic rules for reasoning. This paradigm naturally captures long-range interdependence due to the chains’ compositional nature, which also improves interpretability by explicitly modeling the reasoning process. We introduce T-norm fuzzy logic for optimization, which permits end-to-end learning and shows promise for integrating the expressiveness of logical reasoning with the generalization of neural networks. In experiments, we show that our approach outperforms previous methods by a significant margin on two standard benchmarks (over 6 points in F1).Moreover, it is data-efficient in low-resource scenarios and robust enough to defend against adversarial attacks.- Anthology ID:
- 2023.acl-long.532
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9570–9583
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.532
- DOI:
- 10.18653/v1/2023.acl-long.532
- Cite (ACL):
- Jian Liu, Chen Liang, Jinan Xu, Haoyan Liu, and Zhe Zhao. 2023. Document-Level Event Argument Extraction With a Chain Reasoning Paradigm. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9570–9583, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Document-Level Event Argument Extraction With a Chain Reasoning Paradigm (Liu et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2023.acl-long.532.pdf