Legal Judgment Prediction via Event Extraction with Constraints

Yi Feng; Chuanyi Li; Vincent Ng

ACL 2022long87 citations

Legal Judgment Prediction via Event Extraction with Constraints

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.

BibTeX

@inproceedings{feng-etal-2022-legal,
    title = "Legal Judgment Prediction via Event Extraction with Constraints",
    author = "Feng, Yi  and
      Li, Chuanyi  and
      Ng, Vincent",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    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"
}