README.md

---
language:
- vi
license: cc-by-nc-sa-4.0
size_categories:
- 10K<n<100K
task_categories:
- question-answering
task_ids:
- extractive-qa
pretty_name: VIMQA
tags:
- multi-hop
- vietnamese
- explainable-qa
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
- config_name: gold_only
  data_files:
  - split: validation
    path: gold_only/validation-*
  - split: test
    path: gold_only/test-*
---

# VIMQA

VIMQA is a Vietnamese dataset for advanced reasoning and explainable multi-hop
question answering. Each question requires combining facts from two different
Vietnamese Wikipedia articles, and every example ships with sentence-level
supporting facts so a model's reasoning chain can be evaluated, not just its
final answer.

The schema follows the [HotpotQA](https://huggingface.co/datasets/hotpotqa/hotpot_qa)
convention, so tooling written for HotpotQA transfers with minimal changes.

## Usage

```python
from datasets import load_dataset

# Full distractor setting: 10 paragraphs per question, 2 of them gold.
ds = load_dataset("nguyenlab/vimqa")

# Gold-only setting: just the supporting paragraphs.
gold = load_dataset("nguyenlab/vimqa", "gold_only")
```

## Configs and splits

| Config | Split | Rows | Paragraphs per question |
|---|---|---|---|
| `default` | `train` | 8,041 | 10 |
| `default` | `validation` | 1,003 | 10 |
| `default` | `test` | 1,003 | 10 |
| `gold_only` | `validation` | 1,003 | 1–2 |
| `gold_only` | `test` | 1,003 | 1–2 |

The `default` config is the distractor setting: each question comes with 10
candidate paragraphs, of which only the supporting ones are relevant. The
`gold_only` config contains the same questions with distractors removed, which
is useful for isolating reading-comprehension ability from retrieval.

## Fields

| Field | Type | Description |
|---|---|---|
| `id` | `string` | Unique example identifier |
| `question` | `string` | The Vietnamese question |
| `answer` | `string` | The answer span, or a yes/no answer (`đúng` / `không`) |
| `type` | `string` | Reasoning type of the question |
| `context.title` | `list[string]` | Titles of the candidate paragraphs |
| `context.sentences` | `list[list[string]]` | Each paragraph, split into sentences |
| `supporting_facts.title` | `list[string]` | Titles of paragraphs containing supporting facts |
| `supporting_facts.sent_id` | `list[int32]` | Index into that paragraph's `sentences` list |

A supporting fact is the pair (`title`, `sent_id`): it points at one specific
sentence inside one specific context paragraph.

### Example

```python
{
  "id": "aebce1bf-35a3-4e0c-85c1-e59b24dfb48b",
  "question": "Diego Maradona nhỏ tuổi hơn Rutherford B. Hayes phải không?",
  "answer": "đúng",
  "type": "bridge",
  "context": {
    "title": ["PH", "Diego Maradona", "Rutherford B. Hayes", ...],
    "sentences": [["Các dung dịch nước có giá trị pH nhỏ hơn 7 ..."], [...], [...]]
  },
  "supporting_facts": {
    "title": ["Diego Maradona", "Rutherford B. Hayes"],
    "sent_id": [0, 0]
  }
}
```

To recover the text of the supporting sentences:

```python
def supporting_sentences(example):
    lookup = dict(zip(example["context"]["title"], example["context"]["sentences"]))
    return [
        lookup[title][sent_id]
        for title, sent_id in zip(
            example["supporting_facts"]["title"],
            example["supporting_facts"]["sent_id"],
        )
    ]
```

## Source data

Contexts are drawn from Vietnamese Wikipedia. Questions and supporting-fact
annotations were written by human annotators.

## Citation

```bibtex
@inproceedings{le-etal-2022-vimqa,
    title = "{VIMQA}: A {V}ietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering",
    author = "Le, Khang and Nguyen, Hien and Le Thanh, Tung and Nguyen, Minh",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.700",
    pages = "6521--6529",
}
```