This paper describes our submission to the SemEval-2023 for Task 6 on LegalEval: Understanding Legal Texts. Our submission concentrated on three subtasks: Legal Named Entity Recognition (L-NER) for Task-B, Legal Judgment Prediction (LJP) for Task-C1, and Court Judgment Prediction with Explanation (CJPE) for Task-C2. We conducted various experiments on these subtasks and presented the results in detail, including data statistics and methodology. It is worth noting that legal tasks, such as those tackled in this research, have been gaining importance due to the increasing need to automate legal analysis and support. Our team obtained competitive rankings of 15$^{th}$, 11$^{th}$, and 1$^{st}$ in Task-B, Task-C1, and Task-C2, respectively, as reported on the leaderboard.

翻译：本文描述了我们在SemEval-2023任务6（LegalEval：法律文本理解）中的提交成果。我们的工作聚焦于三个子任务：任务B的法律命名实体识别（L-NER）、任务C1的法律判决预测（LJP）以及任务C2的附带解释的法院判决预测（CJPE）。针对这些子任务，我们开展了多项实验，并详细呈现了实验结果，包括数据统计与方法论。值得注意的是，由于法律分析自动化支持的需求日益增长，诸如本研究所涉及的法律任务正变得越来越重要。根据排行榜报告，我们的团队在任务B、任务C1和任务C2中分别取得了第15名、第11名和第1名的竞争性排名。