DANGDOCAO/GeneratingQuestions

HVU_QA

HVU_QA is an open-source Vietnamese Question–Context–Answer (QCA) corpus, accompanied by supporting tools, created to facilitate the development of FAQ-style question generation and question answering systems, particularly for low-resource language settings. The dataset is developed by a research team at Hung Vuong University, Phu Tho, Vietnam, led by Dr. Ha Nguyen, Deputy Head of the Department of Engineering Technology. HVU_QA was constructed using a fully automated data-building pipeline that combines web crawling from reliable sources, semantic tag-based extraction, and AI-assisted filtering, helping ensure high factual accuracy, consistent structure, and practical usability for real-world applications.

📋 Dataset Description

• Language: Vietnamese
• Format: SQuAD-style JSON
• Total samples: 39,000 QCA triples (full corpus released)
• Domains covered: Social services, labor law, administrative processes, and other public service topics.
• Structure of each sample:
  • Question: Generated or extracted question
  • Context: Supporting text passage from which the answer is derived
  • Answer: Answer span within the context

⚙️ Creation Pipeline

The dataset was built using a 4-stage automated process:

1. Selecting relevant QA websites from trusted sources.
2. Automated data crawling to collect raw QA webpages.
3. Extraction via semantic tags to obtain clean Question–Context–Answer triples.
4. AI-assisted filtering to remove noisy or factually inconsistent samples.

📊 Quality Evaluation

A fine-tuned vit5-base model trained on HVU_QA achieved:

Metric	Score
BLEU	89.1
Semantic similarity	91.5% (cos ≥ 0.8)
Human grammar score	4.58 / 5
Human usefulness score	4.29 / 5

These results confirm that HVU_QA is a high-quality resource for developing robust FAQ-style question generation models.

📁 Dataset Structure

.HVU_QA
├── t5-viet-qg-finetuned/
├── fine_tune_qg.py
├── generate_question.py
├── 39k_train.json
└── README.md

📁 Vietnamese Question Generation Tool

🛠️ Requirements

• Python 3.8+
• PyTorch >= 1.9
• Transformers >= 4.30
• scikit-learn

📦 Install Required Libraries

pip install datasets transformers sentencepiece safetensors accelerate evaluate sacrebleu rouge-score nltk scikit-learn

(Install PyTorch separately from pytorch.org if not installed yet.)

📥 Load Dataset from Hugging Face Hub

from datasets import load_dataset

ds = load_dataset("DANGDOCAO/GeneratingQuestions", split="train")
print(ds[0])

📚 Usage

• Train and evaluate a question generation model.
• Develop Vietnamese NLP tools.
• Conduct linguistic research.

🔹 Fine-tuning

python fine_tune_qg.py

This will:

1. Load the dataset from 39k_train.json.
2. Fine-tune VietAI/vit5-base.
3. Save the trained model into t5-viet-qg-finetuned/.

(Or download the pre-trained model: t5-viet-qg-finetuned.)

🔹 Generating Questions

python generate_question.py

Example:

Input passage:
Cà phê sữa đá là một loại đồ uống nổi tiếng ở Việt Nam
(Iced milk coffee is a famous drink in Vietnam)

Number of questions: 5

Output:

1. Loại cà phê nào nổi tiếng ở Việt Nam?  
   (What type of coffee is famous in Vietnam?)  
2. Tại sao cà phê sữa đá lại phổ biến?  
   (Why is iced milk coffee popular?)  
3. Cà phê sữa đá bao gồm những nguyên liệu gì?  
   (What ingredients are included in iced milk coffee?)  
4. Cà phê sữa đá có nguồn gốc từ đâu?  
   (Where does iced milk coffee originate from?)  
5. Cà phê sữa đá Việt Nam được pha chế như thế nào?  
   (How is Vietnamese iced milk coffee prepared?)  

You can adjust in generate_question.py:

• top_k, top_p, temperature, no_repeat_ngram_size, repetition_penalty

📌 Citation

If you use HVU_QA in your research, please cite:

@inproceedings{nguyen2025method,
  author    = {Ha Nguyen and Phuc Le and Dang Do and Cuong Nguyen and Chung Mai},
  title     = {A Method for Building QA Corpora for Low-Resource Languages},
  booktitle = {Proceedings of the 2025 International Symposium on Information and Communication Technology (SOICT 2025)},
  year      = {2025},
  publisher = {Springer},
  series    = {Communications in Computer and Information Science (CCIS)},
  address   = {Nha Trang, Vietnam},
  note      = {To appear}
}

❤️ Support / Funding

If you find HVU_QA useful, please consider supporting our work.
Your contributions help us maintain the dataset, improve quality, and release new versions (cleaning, expansion, benchmarks, and tools).

🇻🇳 Donate via VietQR (scan to support)

This VietQR / NAPAS 247 code can be scanned by Vietnamese banking apps and some international payment apps that support QR bank transfers.

Bank: VietinBank (Vietnam)
Account name: NGUYEN TIEN HA
Account number: 103004492490
Branch: VietinBank CN PHU THO - HOI SO

🌍 International Support (Quick card payment)

If you are outside Vietnam, you can support this project via Buy Me a Coffee
(no PayPal account needed — pay directly with a credit/debit card):

• BuyMeACoffee: https://buymeacoffee.com/hanguyen0408

🌍 International Support (PayPal)

If you prefer PayPal, you can also support us here:

• PayPal.me: https://paypal.me/HaNguyen0408

✨ Other ways to support

• ⭐ Star this repository / dataset on Hugging Face
• 📌 Cite our paper if you use it in your research
• 🐛 Open issues / pull requests to improve the dataset and tools
• 📬 Contact / Maintainers For questions, feedback, collaborations, or issue reports related to HVU_QA, please contact: Dr. Ha Nguyen (Project Lead) Hung Vuong University, Phu Tho, Vietnam Email: nguyentienha@hvu.edu.vn