---
license: cc-by-nc-4.0
task_categories:
- translation
language:
- vi
size_categories:
- 10K<n<100K
pretty_name: ViDia2Std
tags:
- dialect-normalization
- vietnamese-dialect
- social-media
- low-resource
configs:
- config_name: default
  data_files:
  - split: train
    path: train.csv
  - split: validation
    path: dev.csv
  - split: test
    path: test.csv
---

# ViDia2Std: A Parallel Corpus for Vietnamese Dialect-to-Standard Translation

**ViDia2Std** is a high-quality, manually annotated parallel corpus for translating Vietnamese dialects (Northern, Central, Southern) into Standard Vietnamese. Sourced from authentic social media comments across all 63 provinces of Vietnam, it serves as a benchmark for dialect normalization and adaptation tasks.

This dataset accompanies the paper: **"ViDia2Std: A Parallel Corpus and Methods for Low-Resource Vietnamese Dialect-to-Standard Translation"** (Accepted at AAAI-26).

## 📂 Dataset Structure

The dataset is split into three files:

* **`train.csv`**: The training set (10,870 pairs).
* **`dev.csv`**: The development/validation set (1,184 pairs).
* **`test.csv`**: The testing set (1,603 pairs).

### 📝 Data Fields

| Column | Description |
| :--- | :--- |
| **`dialect`** | The original dialectal input sentence (source). Contains regional words, and non-standard grammar. |
| **`standard`** | The manually normalized Standard Vietnamese sentence (target). Equivalent in meaning and intent to the source. |
| **`region`** | The dialect region of the source sentence: `northern`, `central`, or `southern`. |
| **`sentiment`** | *(Only in `test.csv`)* The sentiment label of the sentence (`positive`, `negative`, `neutral`). Used for extrinsic evaluation. |

## 📊 Statistics

| Split | Samples |
| :--- | :--- |
| **Train** | 10,870 |
| **Dev** | 1,184 |
| **Test** | 1,603 |
| **Total** | **13,657** |

## 🚀 Usage

### Loading with Pandas
```python
import pandas as pd

train_df = pd.read_csv("train.csv")
print(train_df.head())

# Accessing specific columns
inputs = train_df['dialect']
targets = train_df['standard']