README.md

Dataset Card for HVU_QA

HVU_QA is an open-source Vietnamese Question–Context–Answer (QCA) corpus and supporting tools for building FAQ-style question generation systems in low-resource languages.
The dataset was created using a fully automated pipeline that combines web crawling from trustworthy sources, semantic tag-based extraction, and AI-assisted filtering to ensure high factual accuracy.

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Dataset Summary

• Language: Vietnamese
• Format: SQuAD-style JSON
• Total samples: 30,000 QCA triples (full corpus released)
• Domains covered: Social services, labor law, administrative processes, and other public service topics

Each entry in the dataset has the following structure:

• Question: Generated or extracted question
• Context: Supporting text passage from which the answer is derived
• Answer: Answer span within the context

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Supported Tasks and Benchmarks

• Question Generation (QG)
• Question Answering (QA)
• FAQ-style dialogue systems

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

• BLEU: 90.61
• Semantic similarity: 97.0% (cosine similarity ≥ 0.8)
• Human evaluation:
  • Grammaticality: 4.58 / 5
  • Usefulness: 4.29 / 5

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Languages

• Vietnamese (primary)

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Dataset Structure

Data Fields

Each sample contains:

• question: A natural language question
• context: Supporting text passage
• answer: The extracted answer span

Data Splits

Split	Size
Train	30,000

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Dataset Creation

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 Q–C–A triples
4. AI-assisted filtering to remove noisy or factually inconsistent samples

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Usage Example

from datasets import load_dataset

dataset = load_dataset("DANGDOCAO/GeneratingQuestions")
print(dataset["train"][0])

Example output:

{
  "question": "What type of coffee is famous in Vietnam?",
  "context": "Iced milk coffee is a famous drink in Vietnam.",
  "answer": "Iced milk coffee"
}

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Training & Fine-tuning

To fine-tune a question generation model:

python fine_tune_qg.py

• Loads 30ktrain.json
• Fine-tunes VietAI/vit5-base
• Saves model as t5-viet-qg-finetuned/

👉 Alternatively, you can use the pre-trained model provided here:
Pre-trained model link

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Question Generation Example

python generate_question.py

Input passage:

Iced milk coffee is a famous drink in Vietnam.

Generated questions:

1. What type of coffee is famous in Vietnam?
2. Why is iced milk coffee popular?
3. What ingredients are included in iced milk coffee?
4. Where does iced milk coffee originate from?
5. How is Vietnamese iced milk coffee prepared?

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Citation

If you use HVU_QA in your research, please cite:

@inproceedings{nguyen2025hvuqa,
  title={A Method to Build QA Corpora for Low-Resource Languages},
  author={Ha Nguyen-Tien and Phuc Le-Hong and Dang Do-Cao and Cuong Nguyen-Hung and Chung Mai-Van},
  booktitle={Proceedings of the International Conference on Knowledge and Systems Engineering (KSE)},
  year={2025}
}

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License

This dataset is released for research purposes only under the CC BY-NC-SA 4.0 license.