The growth of pending legal cases in populous countries, such as India, has
become a major issue. Developing effective techniques to process and understand
legal documents is extremely useful in resolving this problem. In this paper,
we present our systems for SemEval-2023 Task 6: understanding legal texts (Modi
et al., 2023). Specifically, we first develop the Legal-BERT-HSLN model that
considers the comprehensive context information in both intra- and
inter-sentence levels to predict rhetorical roles (subtask A) and then train a
Legal-LUKE model, which is legal-contextualized and entity-aware, to recognize
legal entities (subtask B). Our evaluations demonstrate that our designed
models are more accurate than baselines, e.g., with an up to 15.0% better F1
score in subtask B. We achieved notable performance in the task leaderboard,
e.g., 0.834 micro F1 score, and ranked No.5 out of 27 teams in subtask A.

针对印度等人口密集的国家法律案件增长的问题，本论文提出了解决该问题的有效技术 ——SemEval-2023 任务 6：理解法律文本的系统，该系统利用 Legal-BERT-HSLN 模型和 Legal-LUKE 模型预测法律文件的修辞角色和识别法律实体，并表明模型优于基线模型，在领先的任务排行榜中取得显著成绩。