Codes for "Legal Judgment Prediction via Event Extraction with Constraints", accepted by ACL 2022.
Currently, we release a subset of LJP-E.
Citation:
@inproceedings{feng-etal-2022-legal,
title = "Legal Judgment Prediction via Event Extraction with Constraints",
author = "Feng, Yi and
Li, Chuanyi and
Ng, Vincent",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.48",
doi = "10.18653/v1/2022.acl-long.48",
pages = "648--664",
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.",
}