THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained
  Language Models for Legal Case Retrieval
Haitao Li Weihang Su Changyue Wang Yueyue Wu Qingyao Ai Yiqun Liu
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.