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NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment

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arxiv 2309.05500 v1 pith:MVUWFATS submitted 2023-09-11 cs.CL cs.AI

Hai-Long Nguyen , Dieu-Quynh Nguyen , Hoang-Trung Nguyen , Thu-Trang Pham , Huu-Dong Nguyen , Thach-Anh Nguyen , Thi-Hai-Yen Vuong , Ha-Thanh Nguyen This is my paper · ORCID
classification cs.CLcs.AI
keywords legalquestiondomainlanguageslow-resourceacquisitionalqacanswering
verification ladder T0 review T1 audit T2 compute T3 formal
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In recent years, natural language processing has gained significant popularity in various sectors, including the legal domain. This paper presents NeCo Team's solutions to the Vietnamese text processing tasks provided in the Automated Legal Question Answering Competition 2023 (ALQAC 2023), focusing on legal domain knowledge acquisition for low-resource languages through data enrichment. Our methods for the legal document retrieval task employ a combination of similarity ranking and deep learning models, while for the second task, which requires extracting an answer from a relevant legal article in response to a question, we propose a range of adaptive techniques to handle different question types. Our approaches achieve outstanding results on both tasks of the competition, demonstrating the potential benefits and effectiveness of question answering systems in the legal field, particularly for low-resource languages.

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