Abstract
An important problem of Information Extraction involves Event Causality Identification (ECI) that seeks to identify causal relation between pairs of event mentions. Prior models for ECI have mainly solved the problem using the classification framework that does not explore prediction/generation of important context words from input sentences for causal recognition. In this work, we consider the words along the dependency path between the two event mentions in the dependency tree as the important context words for ECI. We introduce dependency path generation as a complementary task for ECI, which can be solved jointly with causal label prediction to improve the performance. To facilitate the multi-task learning, we cast ECI into a generation problem that aims to generate both causal relation and dependency path words from input sentence. In addition, we propose to use the REINFORCE algorithm to train our generative model where novel reward functions are designed to capture both causal prediction accuracy and generation quality. The experiments on two benchmark datasets demonstrate state-of-the-art performance of the proposed model for ECI.- Anthology ID:
- 2022.starsem-1.28
- Volume:
- Proceedings of the 11th Joint Conference on Lexical and Computational Semantics
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, Washington
- Editors:
- Vivi Nastase, Ellie Pavlick, Mohammad Taher Pilehvar, Jose Camacho-Collados, Alessandro Raganato
- Venue:
- *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 323–330
- Language:
- URL:
- https://aclanthology.org/2022.starsem-1.28
- DOI:
- 10.18653/v1/2022.starsem-1.28
- Cite (ACL):
- Hieu Man, Minh Nguyen, and Thien Nguyen. 2022. Event Causality Identification via Generation of Important Context Words. In Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, pages 323–330, Seattle, Washington. Association for Computational Linguistics.
- Cite (Informal):
- Event Causality Identification via Generation of Important Context Words (Man et al., *SEM 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.starsem-1.28.pdf
- Code
- hieumdt/GenECI