This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task. This task requires the participant to identify a specific paragraph from a given supporting case that entails the decision for the query case. We try traditional lexical matching methods and pre-trained language models with different sizes. Furthermore, learning-to-rank methods are employed to further improve performance. However, learning-to-rank is not very robust on this task. which suggests that answer passages cannot simply be determined with information retrieval techniques. Experimental results show that more parameters and legal knowledge contribute to the legal case entailment task. Finally, we get the third place in COLIEE 2023. The implementation of our method can be found at https://github.com/CSHaitao/THUIR-COLIEE2023.

翻译：本文描述了THUIR团队在COLIEE 2023法律案例蕴含任务中的方法。该任务要求参与者从给定的支持案例中找出蕴含查询案例判决的具体段落。我们尝试了传统词汇匹配方法以及不同规模的预训练语言模型。此外，采用学习排序方法以进一步提升性能。然而，学习排序在该任务上并不十分稳健，这表明答案段落不能简单地通过信息检索技术来确定。实验结果表明，更多参数和法律知识有助于法律案例蕴含任务。最终，我们在COLIEE 2023中获得了第三名。我们方法的实现可在https://github.com/CSHaitao/THUIR-COLIEE2023获取。