@inproceedings{li-etal-2025-legal,
    title = "Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification",
    author = "Li, Ang  and
      Wu, Yiquan  and
      Cai, Ming  and
      Jatowt, Adam  and
      Zhou, Xiang  and
      Lu, Weiming  and
      Sun, Changlong  and
      Wu, Fei  and
      Kuang, Kun",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.355/",
    pages = "6957--6970",
    ISBN = "979-8-89176-189-6",
    abstract = "Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case. Legal judgments can involve multiple law articles and charges. Although recent methods in LJP have made notable progress, most are constrained to single-task settings (e.g., only predicting charges) or single-label settings (e.g., not accommodating cases with multiple charges), diverging from the complexities of real-world scenarios. In this paper, we address the challenge of predicting relevant law articles and charges within the framework of legal judgment prediction, treating it as a multi-task and multi-label text classification problem. We introduce a knowledge-enhanced approach, called K-LJP, that incorporates (I) ``label-level knowledge'' (such as definitions and relationships among labels) to enhance the representation of case facts for each task, and (ii) ``task-level knowledge'' (such as the alignment between law articles and corresponding charges) to improve task synergy. Comprehensive experiments demonstrate our method{'}s effectiveness in comparison to state-of-the-art (SOTA) baselines."
}