Summary of R2gqa: Retriever-reader-generator Question Answering System to Support Students Understanding Legal Regulations in Higher Education, by Phuc-tinh Pham Do et al.
R2GQA: Retriever-Reader-Generator Question Answering System to Support Students Understanding Legal Regulations in Higher Education
by Phuc-Tinh Pham Do, Duy-Ngoc Dinh Cao, Khanh Quoc Tran, Kiet Van Nguyen
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed R2GQA system is a Retriever-Reader-Generator Question Answering model that consists of three main components: Document Retriever, Machine Reader, and Answer Generator. The Retriever module retrieves articles from legal regulation documents using advanced information retrieval techniques, while the Machine Reader module utilizes natural language understanding algorithms to comprehend the retrieved documents and extract answers. The Answer Generator module synthesizes the extracted answers into concise responses to student questions regarding legal regulations. A new dataset, ViRHE4QA, is also introduced in the domain of university training regulations, containing 9,758 question-answer pairs with various types of answers (extractive and abstractive). The R2GQA system demonstrates effectiveness and utility in supporting students’ comprehension of legal regulations in higher education settings. The proposed system and dataset aim to contribute significantly to related research and empower students to navigate complex legal documents and regulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to help students understand legal regulations. It’s called the R2GQA system, which is made up of three parts: one that finds relevant articles, one that reads and understands those articles, and one that answers student questions about the articles. The system uses advanced technology to retrieve articles from a large dataset of legal documents. A new set of question-answer pairs was also created specifically for this project. This data includes both simple and complex answers. The system’s goal is to help students understand and navigate complex legal documents, making it easier for them to make informed decisions. |
Keywords
* Artificial intelligence * Language understanding * Question answering
