Legal Judgment Prediction via Event Extraction with Constraints

Yi Feng, Chuanyi Li, Vincent Ng


Abstract
While significant progress has been made on the task of Legal Judgment Prediction (LJP) in recent years, the incorrect predictions made by SOTA LJP models can be attributed in part to their failure to (1) locate the key event information that determines the judgment, and (2) exploit the cross-task consistency constraints that exist among the subtasks of LJP. To address these weaknesses, we propose EPM, an Event-based Prediction Model with constraints, which surpasses existing SOTA models in performance on a standard LJP dataset.
Anthology ID:
2022.acl-long.48
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
648–664
Language:
URL:
https://aclanthology.org/2022.acl-long.48
DOI:
10.18653/v1/2022.acl-long.48
Bibkey:
Cite (ACL):
Yi Feng, Chuanyi Li, and Vincent Ng. 2022. Legal Judgment Prediction via Event Extraction with Constraints. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 648–664, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Legal Judgment Prediction via Event Extraction with Constraints (Feng et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.48.pdf
Software:
 2022.acl-long.48.software.zip
Code
 wapay/epm