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：本文旨在解决像印度这样人口众多的国家中未决法律案件的快速增长问题。为了达成这一目标，我们需要开发有效的技术来处理和理解法律文件。在本文中，我们展示了我们为SemEval-2023任务6开发的文本理解系统（Modi等人，2023）。具体来说，我们首先开发了Legal-BERT-HSLN模型，该模型考虑了句内和句间的广泛上下文信息以预测修辞角色（子任务A），然后训练了Legal-LUKE模型，该模型是法律上下文相关且能够识别法律实体的（子任务B）。我们的评估结果表明，我们设计的模型比基线模型更准确，例如，在子任务B中F1分数高达15.0％。我们在任务排行榜上获得了显着的表现，例如，0.834微F1分数，在子任务A中排名第5位（共27个团队）。
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