Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
Vietnamese
Size:
10K - 100K
License:
File size: 4,242 Bytes
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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",
}
```
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