CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information Retrieval and Entailment Tasks
This work addresses the challenge of processing complex legal texts for legal AI applications, but it is incremental as it applies existing methods to a competition setting.
The paper tackled legal information retrieval and entailment tasks in the COLIEE 2023 competition by applying state-of-the-art deep learning methods and domain-specific engineering, achieving first place in Task 2 and Task 3 and promising results in Task 4.
The Competition on Legal Information Extraction/Entailment (COLIEE) is held annually to encourage advancements in the automatic processing of legal texts. Processing legal documents is challenging due to the intricate structure and meaning of legal language. In this paper, we outline our strategies for tackling Task 2, Task 3, and Task 4 in the COLIEE 2023 competition. Our approach involved utilizing appropriate state-of-the-art deep learning methods, designing methods based on domain characteristics observation, and applying meticulous engineering practices and methodologies to the competition. As a result, our performance in these tasks has been outstanding, with first places in Task 2 and Task 3, and promising results in Task 4. Our source code is available at https://github.com/Nguyen2015/CAPTAIN-COLIEE2023/tree/coliee2023.