Multi-stage Information Retrieval for Vietnamese Legal Texts
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 29 Sep 2022.
Builds on
12 of 25 references.
References · 12
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
- 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 695
- 2021Condenser: a Pre-training Architecture for Dense Retrievalcited by 162
- 2020PhoBERT: Pre-trained language models for Vietnamesecited by 149
- 2020Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Taskscited by 138
- 2020SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Modelscited by 56
- 2018VnCoreNLP: A Vietnamese Natural Language Processing Toolkitcited by 51
- 2020Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Modelscited by 22
- 2022Toward Improving Attentive Neural Networks in Legal Text Processingcited by 1
Built on by
Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.