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

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arxiv 2401.03551 v1 pith:JW644QZ7 submitted 2024-01-07 cs.CL cs.IR

Chau Nguyen , Phuong Nguyen , Thanh Tran , Dat Nguyen , An Trieu , Tin Pham , Anh Dang , Le-Minh Nguyen This is my paper · ORCID
classification cs.CLcs.IR
keywords tasklegalcolieecompetitionmethodsentailmentinformationprocessing
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. AI for Statutory Simplification: A Comprehensive State Legal Corpus and Labor Benchmark

    cs.IR 2025-08 conditional novelty 7.0 of 10

    State-of-the-art LLMs with retrieval answer simplified boolean questions about state unemployment insurance law with at best 0.69 F1, well short of reliable end-to-end code simplification.

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