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

TL;DR

This paper introduces methods for acquiring legal domain knowledge in low-resource languages by enriching data, demonstrating effective question answering techniques in Vietnamese legal texts with strong competition results.

Contribution

The paper presents novel data enrichment and adaptive question answering techniques tailored for low-resource legal language processing.

Findings

Achieved outstanding results on legal document retrieval and question answering tasks.

Demonstrated the effectiveness of data enrichment in low-resource legal NLP.

Validated approaches through competitive performance in ALQAC 2023.

Abstract

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.