mlsanityData to 7 Oct 2026

R2GQA: Retriever-Reader-Generator Question Answering System to Support Students Understanding Legal Regulations in Higher Education

arXiv 2409.02840 ↗First posted 4 Sep 2024cs.CL cs.AIcited by 2

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Abstract (arXiv)

In this article, we propose the R2GQA system, a Retriever-Reader-Generator Question Answering system, consisting of three main components: Document Retriever, Machine Reader, and Answer Generator. The Retriever module employs advanced information retrieval techniques to extract the context of articles from a dataset of legal regulation documents. The Machine Reader module utilizes state-of-the-art natural language understanding algorithms to comprehend the retrieved documents and extract answers. Finally, the Generator module synthesizes the extracted answers into concise and informative responses to questions of students regarding legal regulations. Furthermore, we built the ViRHE4QA dataset in the domain of university training regulations, comprising 9,758 question-answer pairs with a rigorous construction process. This is the first Vietnamese dataset in the higher regulations domain with various types of answers, both extractive and abstractive. In addition, the R2GQA system is the first system to offer abstractive answers in Vietnamese. This paper discusses the design and implementation of each module within the R2GQA system on the ViRHE4QA dataset, highlighting their functionalities and interactions. Furthermore, we present experimental results demonstrating the effectiveness and utility of the proposed system in supporting the comprehension of students of legal regulations in higher education settings. In general, the R2GQA system and the ViRHE4QA dataset promise to contribute significantly to related research and help students navigate complex legal documents and regulations, empowering them to make informed decisions and adhere to institutional policies effectively. Our dataset is available for research purposes.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 40 references that are in the map. First posted on arXiv 4 Sep 2024.

Builds on

28 of 40 references.

References · 28

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  7. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  8. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
  9. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  10. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  11. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
  12. 2020Multilingual Translation with Extensible Multilingual Pretraining and Finetuningcited by 287
16 more
  1. 2019End-to-End Open-Domain Question Answering with BERTserinicited by 152
  2. 2020Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillationcited by 143
  3. 2019JEC-QA: A Legal-Domain Question Answering Datasetcited by 115
  4. 2019Question Answering for Privacy Policies: Combining Computational and Legal Perspectivescited by 65
  5. 2018What Makes Reading Comprehension Questions Easier?cited by 59
  6. 2022ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generationcited by 37
  7. 2021BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamesecited by 33
  8. 2020A Vietnamese Dataset for Evaluating Machine Reading Comprehensioncited by 30
  9. 2020New Vietnamese Corpus for Machine Reading Comprehension of Health News Articlescited by 23
  10. 2020Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Modelscited by 22
  11. 2021Sentence Extraction-Based Machine Reading Comprehension for Vietnamesecited by 9
  12. 2024VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understandingcited by 8
  13. 2020Multi-span Style Extraction for Generative Reading Comprehensioncited by 7
  14. 2022Context Generation Improves Open Domain Question Answeringcited by 7
  15. 2022Expert Finding in Legal Community Question Answeringcited by 5
  16. 2022XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Sourcecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025A Benchmark Dataset and Evaluation Framework for Vietnamese Large Language Models in Customer Supportcited by 0
  2. 2026ViHERMES: A Graph-Grounded Multihop Question Answering Benchmark and System for Vietnamese Healthcare Regulationscited by 0