Improving Vietnamese Legal Document Retrieval using Synthetic Data
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912139199905792 |
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| author | Tien, Son Pham Doan, Hieu Nguyen Dai, An Nguyen Viet, Sang Dinh |
| author_facet | Tien, Son Pham Doan, Hieu Nguyen Dai, An Nguyen Viet, Sang Dinh |
| contents | In the field of legal information retrieval, effective embedding-based models are essential for accurate question-answering systems. However, the scarcity of large annotated datasets poses a significant challenge, particularly for Vietnamese legal texts. To address this issue, we propose a novel approach that leverages large language models to generate high-quality, diverse synthetic queries for Vietnamese legal passages. This synthetic data is then used to pre-train retrieval models, specifically bi-encoder and ColBERT, which are further fine-tuned using contrastive loss with mined hard negatives. Our experiments demonstrate that these enhancements lead to strong improvement in retrieval accuracy, validating the effectiveness of synthetic data and pre-training techniques in overcoming the limitations posed by the lack of large labeled datasets in the Vietnamese legal domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00657 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Vietnamese Legal Document Retrieval using Synthetic Data Tien, Son Pham Doan, Hieu Nguyen Dai, An Nguyen Viet, Sang Dinh Information Retrieval Artificial Intelligence In the field of legal information retrieval, effective embedding-based models are essential for accurate question-answering systems. However, the scarcity of large annotated datasets poses a significant challenge, particularly for Vietnamese legal texts. To address this issue, we propose a novel approach that leverages large language models to generate high-quality, diverse synthetic queries for Vietnamese legal passages. This synthetic data is then used to pre-train retrieval models, specifically bi-encoder and ColBERT, which are further fine-tuned using contrastive loss with mined hard negatives. Our experiments demonstrate that these enhancements lead to strong improvement in retrieval accuracy, validating the effectiveness of synthetic data and pre-training techniques in overcoming the limitations posed by the lack of large labeled datasets in the Vietnamese legal domain. |
| title | Improving Vietnamese Legal Document Retrieval using Synthetic Data |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2412.00657 |