CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information Retrieval and Entailment Tasks

Chau Nguyen, Phuong Nguyen, Thanh Tran, Dat Nguyen, An Trieu +3 more
1/7/2024
cs.CLcs.IR

Official code: Nguyen2015/CAPTAIN-COLIEE2023 (5 stars, MIT, last commit 2024-07-23).

Abstract

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.

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Code Implementations(1)

Nguyen2015/CAPTAIN-COLIEE2023Official100%
MIT
51Shell, PythonMay 16, 20232 years agoMIT

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Cite this paper

@article{nguyen2024captain,
  title  = {CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information Retrieval and Entailment Tasks},
  author = {Chau Nguyen and Phuong Nguyen and Thanh Tran and Dat Nguyen and An Trieu and Tin Pham and Anh Dang and Le-Minh Nguyen},
  year   = {2024},
  eprint = {2401.03551},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.03551v1}
}

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