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Multi-stage Information Retrieval for Vietnamese Legal Texts

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arxiv 2209.14494 v2 pith:CI3GFV6Z submitted 2022-09-29 cs.CL

Nhat-Minh Pham , Ha-Thanh Nguyen , Trong-Hop Do This is my paper · ORCID
classification cs.CL
keywords informationretrievalvietnameselegaldocumentsmodelsresearchtexts
verification ladder T0 review T1 audit T2 compute T3 formal
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This study deals with the problem of information retrieval (IR) for Vietnamese legal texts. Despite being well researched in many languages, information retrieval has still not received much attention from the Vietnamese research community. This is especially true for the case of legal documents, which are hard to process. This study proposes a new approach for information retrieval for Vietnamese legal documents using sentence-transformer. Besides, various experiments are conducted to make comparisons between different transformer models, ranking scores, syllable-level, and word-level training. The experiment results show that the proposed model outperforms models used in current research on information retrieval for Vietnamese documents.

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Cited by 2 Pith papers

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  1. Adaptive Two-Phase Finetuning LLMs for Japanese Legal Text Retrieval

    cs.IR 2024-12 conditional novelty 4.0 of 10

    A two-phase fine-tuning pipeline (global contrastive pretraining, then domain-specific hard-negative training) improves LLaMA-based dense retrieval on a Gemini-generated Japanese legal dataset and on a subset of MS MARCO.

  2. Improving Vietnamese Legal Document Retrieval using Synthetic Data

    cs.IR 2024-12 conditional novelty 4.0 of 10

    Llama 3 generated 507,152 synthetic Vietnamese legal queries; using them for CoT-MAE pre-training and contrastive fine-tuning improved bi-encoder and ColBERT retrieval scores on TVPL and Legal Zalo 21.

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