论文标题
Thuir@Coliee-2020：利用语义理解和确切的法律案例检索和匹配
THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment
论文作者
论文摘要
在本文中，我们介绍了应对法律案件检索和2020年法律信息提取 /索引竞赛中法律案件检索的挑战（Coliee-2020）的挑战。我们参与了两项案件法律任务，即法律案件检索任务和法律案件的任务。任务1（检索任务）旨在自动从给定新案件的案例法语料库中自动确定案例，而任务2（需要任务）以确定在相关案例中需要新案件决定的特定段落。在这两个任务中，我们都采用了神经模型来进行语义理解和传统的检索模型进行确切匹配。结果，我们的团队（TLIR）在任务1中的所有团队中排名第二，在任务2中的团队中排名第三。实验结果表明，梳理语义理解和确切匹配的模型有益于法律案例检索任务，而法律案件的任务则更多地依赖于语义理解。
In this paper, we present our methodologies for tackling the challenges of legal case retrieval and entailment in the Competition on Legal Information Extraction / Entailment 2020 (COLIEE-2020). We participated in the two case law tasks, i.e., the legal case retrieval task and the legal case entailment task. Task 1 (the retrieval task) aims to automatically identify supporting cases from the case law corpus given a new case, and Task 2 (the entailment task) to identify specific paragraphs that entail the decision of a new case in a relevant case. In both tasks, we employed the neural models for semantic understanding and the traditional retrieval models for exact matching. As a result, our team (TLIR) ranked 2nd among all of the teams in Task 1 and 3rd among teams in Task 2. Experimental results suggest that combing models of semantic understanding and exact matching benefits the legal case retrieval task while the legal case entailment task relies more on semantic understanding.
