THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval

• URL: http://arxiv.org/abs/2305.06812v1
• Date: Thu, 11 May 2023 14:08:53 GMT
• Title: THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval
• Authors: Haitao Li, Weihang Su, Changyue Wang, Yueyue Wu, Qingyao Ai, Yiqun Liu
• Abstract summary: This paper summarizes the approach of the championship team THUIR in COLIEE 2023. To be specific, we design structure-aware pre-trained language models to enhance the understanding of legal cases. In the end, learning-to-rank methods are employed to merge features with different dimensions.
• Score: 16.191450092389722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Legal case retrieval techniques play an essential role in modern intelligent legal systems. As an annually well-known international competition, COLIEE is aiming to achieve the state-of-the-art retrieval model for legal texts. This paper summarizes the approach of the championship team THUIR in COLIEE 2023. To be specific, we design structure-aware pre-trained language models to enhance the understanding of legal cases. Furthermore, we propose heuristic pre-processing and post-processing approaches to reduce the influence of irrelevant messages. In the end, learning-to-rank methods are employed to merge features with different dimensions. Experimental results demonstrate the superiority of our proposal. Official results show that our run has the best performance among all submissions. The implementation of our method can be found at https://github.com/CSHaitao/THUIR-COLIEE2023.

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