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：在印度等人口大国，待审案件数量的增长已成为一个重大问题。开发有效的法律文档处理与理解技术对于解决这一难题极为关键。本文介绍了我们在SemEval-2023任务6（法律文本理解）中提出的系统（Modi等人，2023）。具体而言，我们首先构建了Legal-BERT-HSLN模型，该模型通过融合句内与句间多层级上下文信息来预测修辞角色（子任务A）；随后训练了Legal-LUKE模型——一种具备法律上下文感知与实体识别能力的模型——用于法律实体识别（子任务B）。实验评估表明，我们设计的模型相比基线方法具有更高准确性，例如在子任务B中F1分数提升最高达15.0%。在任务排行榜上，我们取得了显著成绩：子任务A中以0.834的微平均F1分数位列27支参赛队伍中的第5名。