@inproceedings{han-zhicheng-2023-case,
    title = "Case Retrieval for Legal Judgment Prediction in Legal Artificial Intelligence",
    author = "Han, Zhang  and
      Zhicheng, Dou",
    editor = "Sun, Maosong  and
      Qin, Bing  and
      Qiu, Xipeng  and
      Jiang, Jing  and
      Han, Xianpei",
    booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2023",
    address = "Harbin, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.ccl-1.68/",
    pages = "801--812",
    language = "eng",
    abstract = "``Legal judgment prediction (LJP) is a basic task in legal artificial intelligence. It consists ofthree subtasks, which are relevant law article prediction, charge prediction and term of penaltyprediction, and gives the judgment results to assist the work of judges. In recent years, many deeplearning methods have emerged to improve the performance of the legal judgment prediction task. The previous methods mainly improve the performance by integrating law articles and the factdescription of a legal case. However, they rarely consider that the judges usually look up historicalcases before making a judgment in the actual scenario. To simulate this scenario, we propose ahistorical case retrieval framework for the legal judgment prediction task. Specifically, we selectsome historical cases which include all categories from the training dataset. Then, we retrieve themost similar Top-k historical cases of the current legal case and use the vector representation ofthese Top-k historical cases to help predict the judgment results. On two real-world legal datasets,our model achieves better results than several state-of-the-art baseline models.''"
}