NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 11 Sep 2023.
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References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questionscited by 2,140
- 2020PhoBERT: Pre-trained language models for Vietnamesecited by 148
- 2022A Summary of the ALQAC 2021 Competitioncited by 9
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