A Hybrid Approach to Enhancing Contextual Information for Vietnam Civil Code Question-Answering

• Quoc-Dai Luong Tran,
• Hai-My Hoang Nguyen,

Vol. 19, No. 1, January 31, 2025
10.3837/tiis.2025.01.005, Download Paper (Free):

• Deep Neural Networks
• Information Retrieval
• Language Model
• Legal
• question-answering

Abstract

Legal question-answering systems face significant challenges due to the complexity of legal language, the need for accurate statute interpretation, and the highly referential nature of legal documents. In practice, lawyers always refer to legal citations when making their arguments. Current models only rely on question-answer pairs during training without taking advantage of legal citations from official legal documents. This study proposes a hybrid approach to enhancing legal question-answering systems. The approach involves using retrieval models to extract contextual information from legal documents. A query mechanism retrieves relevant legal citations, which are then utilized to enhance the language model. This process enriches legal knowledge and improves the model’s ability to generate and interpret legal content. Integrating the retrieved legal context with the fine-tuned language model lays the groundwork for training a question-answering system based on an Encoder-Decoder architecture. This ultimately improves its performance in delivering precise and legally sound answers. We developed a manually labeled question-answering dataset focusing on the Vietnam Civil Code to demonstrate the model’s effectiveness in limited resource situations. Our approach minimizes the need for extensive training data while maintaining the model’s ability to capture legal topics. Our experiments demonstrate that this hybrid architecture significantly improves performance in legal question-answering tasks compared to traditional models, particularly in scenarios where the language model lacks specific knowledge or when training datasets are limited.

Statistics

Cite this article