Datasets:
File size: 2,842 Bytes
eddae66 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | ---
language:
- vi
license: mit
task_categories:
- text-generation
- text2text-generation
tags:
- spelling-correction
- grammatical-error-correction
- synthetic-data
- vietnam
size_categories:
- 100K<n<1M
---
# Vietnamese Spelling Correction Dataset
This dataset contains **978,417** pairs of noisy (source) and clean (target) Vietnamese sentences, designed for training spelling correction models.
The dataset was synthetically generated by injecting realistic noise into a clean Vietnamese corpus.
## Dataset Structure
The dataset is divided into training and testing sets:
- **Train**: 880,575 examples
- **Test**: 97,842 examples
### Data Fields
- `source`: The text with injected errors (input).
- `target`: The original clean text (label).
### Sample
| Source (Noisy) | Target (Clean) | Error Type |
| :--- | :--- | :--- |
| Căn **bật** hai của số âm có thể được **ban** luận... | Căn **bậc** hai của số âm có thể được **bàn** luận... | Phonological + Unaccented |
| ...xuất **hien** **thườg** xuyên **trog** **kák**... | ...xuất **hiện** **thường** xuyên **trong** **các**... | Multiple realistic typos |
| ...máy **tín** bỏ túi đều có phím... | ...máy **tính** bỏ túi đều có phím... | Phonological (nh->n) |
## Noise Generation Logic
The noise was generated using a custom script with a **0.5 noise rate** (approx. 50% of tokens affected) and guaranteed at least one error per sample. The errors mimic real-world Vietnamese typing and spelling mistakes:
1. **Teencode & Lexical Variants** (~40%):
- Syllable contractions: `ng` $\to$ `g`, `nh` $\to$ `h`, `qu` $\to$ `w`, `yê` $\to$ `i`.
- Phonetic substitutions: `ph` $\to$ `f`, `gi` $\to$ `j`, `c/k` $\to$ `k`.
- Dictionary slang: `vợ` $\to$ `vk`, `không` $\to$ `ko`.
2. **Regional Phonological Errors** (~30%):
- **North**: `tr` $\leftrightarrow$ `ch`, `s` $\leftrightarrow$ `x`, `r` $\leftrightarrow$ `d` $\leftrightarrow$ `gi`.
- **South**: `n` $\leftrightarrow$ `ng` (final), `t` $\leftrightarrow$ `c`.
3. **Typing & Mechanical Errors** (~20%):
- **Spatial**: Hitting adjacent keys on QWERTY keyboard.
- **Telex**: Wrong accent codes (`s` $\to$ `d`), double typing (`đ` $\to$ `ddd`).
- **Operations**: Random insertions, deletions, transpositions.
4. **Unaccented** (~10%):
- Removing tone marks (e.g., `trường` $\to$ `truong`).
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("coung21/vi-spelling-correction")
print(dataset["train"][0])
# Output: {'source': '...', 'target': '...'}
```
## Credits
The source data for this dataset was extracted from a **Vietnamese Wikipedia dump**.
The noise was synthetically generated using a custom noise injection pipeline to simulate realistic Vietnamese spelling errors.
|