---
license: mit
task_categories:
- question-answering
language:
- vi
tags:
- question-generation
- nlp
- faq
- low-resource
pretty_name: HVU_QA
size_categories:
- 10K<n<100K
---
# 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.

## 📋 Dataset Description

- **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.
- **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 `VietAI/vit5-base` model trained on HVU_QA achieved:

| Metric                 | Score           |
|-------------------------|----------------|
| BLEU                    | 90.61           |
| Semantic similarity     | 97.0% (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.

## 📁 Data Fields
```
.HVU_QA
├── t5-viet-qg-finetuned/
├── fine_tune_qg.py
├── generate_question.py
├── 30ktrain.json
└── README.md
```
## ⚡ How to Use

### 📦 Install Dependencies

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

*(Install PyTorch separately from [pytorch.org](https://pytorch.org) if not installed yet.)*

### 📥 Load Dataset from Hugging Face Hub
```python
from datasets import load_dataset

ds = load_dataset("DANGDOCAO/GeneratingQuestions", split="train")
print(ds[0])
```
## 🚀 Example Usage

### 🔹 Fine-tuning

```bash
python fine_tune_qg.py
```

This will:

1. Load the dataset from `30ktrain.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](https://huggingface.co/datasets/DANGDOCAO/GeneratingQuestions/tree/main).)*

### 🔹 Generating Questions
```bash
python generate_question.py
```

**Example:**

```
Input passage:
Iced milk coffee is a famous drink in Vietnam.
Number of questions: 5
```

**Output:**

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?  

**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:

```bibtex
@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}
}
```