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 this https URL

THUIR 在 COLIEE 2023 Legal Case Entailment 任务中尝试了传统词汇匹配方法和不同规模的预训练语言模型，使用学习排序方法进一步提高性能，但学习排序在此任务上不够强大，实验结果表明更多参数和法律知识有助于实现，最终在 COLIEE 2023 中获得第三名。