THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment
This work addresses legal case entailment for legal professionals, but it is incremental as it builds on existing methods without major breakthroughs.
The paper tackled the COLIEE 2023 Legal Case Entailment task by testing lexical matching, pre-trained language models of varying sizes, and learning-to-rank methods, finding that more parameters and legal knowledge improved performance, resulting in a third-place finish.
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.