In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams.
翻译:在人口稠密的国家,未决的法律案件呈指数增长。需要开发基于自然语言处理的技术来处理和自动理解法律文件。为了促进 Legal NLP 领域的研究,我们在 SemEval 2023 上组织了共享任务 LegalEval - 理解法律文本。LegalEval 任务分为三个子任务: 任务-A (修辞性角色标记) 是关于自动将法律文件结构化为语义上连贯的单元,任务-B (法律命名实体识别) 处理识别法律文件中的相关实体,任务-C (带解释的法庭判决预测) 探索自动预测法律案件结果以及提供预测解释的可能性。共有26个团队(分布在全球的大约100名参与者)提交了系统论文。在每个子任务中,所提出的系统都优于基线; 但是,仍有很大的改进空间。本文描述了这些任务,并分析了各个团队提出的技术。