README.md

---
license: mit
task_categories:
- text-classification
language:
- vi
pretty_name: ViANLI
size_categories:
- 10K
---
# Dataset Card for “ViANLI”

## Dataset Summary
**ViANLI (Vietnamese Adversarial Natural Language Inference)** is the first adversarial benchmark dataset for Vietnamese NLI, designed to evaluate model robustness against complex linguistic phenomena.
The dataset was constructed using a **human-and-machine-in-the-loop approach** with **multi-round adversarial generation** and **dual human–machine verification**.
ViANLI contains over **10,000 high-quality premise–hypothesis pairs** across 13 diverse domains from Vietnamese news articles.

Each pair is labeled as **entailment**, **contradiction**, or **neutral**, following the standard NLI framework.
The dataset provides a challenging benchmark for evaluating reasoning robustness and supports research in both **Vietnamese** and **multilingual NLI**.

---

## Languages
- **Vietnamese (vi)**

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## Dataset Structure

### Data Instances
Each instance in ViANLI is a JSON line with the following fields:

| Field | Type | Description |
|--------|------|-------------|
| `uid` | `string` | Unique identifier for each instance |
| `premise` | `string` | The premise sentence extracted from Vietnamese news |
| `hypothesis` | `string` | The hypothesis sentence written by annotators |
| `label` | `string` | One of three classes: `entailment`, `neutral`, `contradiction` |

### Data Splits

| Split | Size |
|--------|------|
| train | 8,012 |
| validation | 1,000 |
| test | 1,000 |

---

### Dataset License

- **License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
*(Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International License)*

### Citation Information

If you use this dataset, please cite:

```bibtex
@article{HUYNH2025130109,
title = {A New Benchmark Dataset and Mixture-of-Experts Language Models for Adversarial Natural Language Inference in Vietnamese},
journal = {Expert Systems with Applications},
pages = {130109},
year = {2025},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2025.130109},
url = {https://www.sciencedirect.com/science/article/pii/S095741742503725X},
author = {Tin Van Huynh, Kiet Van Nguyen and Ngan Luu-Thuy Nguyen},
}