Datasets:

coung21
/

vi-spelling-correction

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:

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.
Regional Phonological Errors (~30%):
- North: tr $\leftrightarrow$ ch, s $\leftrightarrow$ x, r $\leftrightarrow$ d $\leftrightarrow$ gi.
- South: n $\leftrightarrow$ ng (final), t $\leftrightarrow$ c.
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.
Unaccented (~10%):
- Removing tone marks (e.g., trường $\to$ truong).

Usage

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.