THUIR @ COLIEE-2020：利用语义理解和精确匹配进行法律案例检索和蕴涵
THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment
摘要：
在本文中，我们提出了我们的方法，以应对法律信息抽取/蕴涵2020（COLIEE-2020）竞争中的法律案例检索和蕴涵的挑战。我们参与了两个案例法任务，即，法律案例检索任务和法律案例蕴涵任务。任务1（检索任务）的目的是自动识别支持案例从判例法语料库给定一个新的情况下，和任务2（蕴涵任务），以确定具体的段落，需要在相关的情况下，一个新的案件的决定。在这两个任务中，我们采用神经模型进行语义理解，并采用传统的检索模型进行精确匹配。因此，我们的团队（TLIR）在任务1的所有团队中排名第二，在任务2的团队中排名第三。实验结果表明，结合语义理解和精确匹配的模式有利于法律案件检索任务，而法律案件蕴涵任务更依赖于语义理解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
