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.

翻译：在印度等人口众多的国家，积压待审法律案件的数量已成为一个重大问题。开发处理和理解法律文档的有效技术对解决这一问题极具价值。本文介绍了我们为SemEval-2023任务6：法律文本理解（Modi等人，2023）所设计的系统。具体而言，我们首先构建了Legal-BERT-HSLN模型，该模型在句内和句间层面整合全面上下文信息以预测修辞角色（子任务A），随后训练了具备法律上下文感知与实体识别能力的Legal-LUKE模型，用于识别法律实体（子任务B）。实验评估表明，所设计的模型比基线方法更准确，例如在子任务B中F1分数提升高达15.0%。我们在任务排行榜上取得了显著成绩，例如在子任务A中以0.834的宏平均F1分数位列27支参赛队伍中的第5名。