---
name: Zarma Noisy Dataset
language:
- dje
version: 1.0.0
license: cc-by-sa-4.0
source: Derived from monolingual Zarma dataset
task_categories:
- text-classification
- text-generation
- fill-mask
- question-answering
size_categories:
- 100K<n<1M
---

# Zarma Noisy Dataset

## Overview

The Zarma Noisy Dataset is a collection of Zarma sentences with artificially introduced noise to simulate human-like errors. This dataset is designed for tasks such as grammatical error correction (GEC), text denoising, and robustness testing in natural language processing (NLP) for low-resource languages like Zarma. It is derived from a clean monolingual Zarma dataset (`monolingual_zarma.jsonl`) by applying various types of noise, including character-level and word-level modifications.

## Dataset Structure

The dataset is stored in a JSONL file (`noisy/zarma_noisy_dataset.jsonl`) where each line is a JSON object with the following fields:

*   **`original`**: The raw input sentence as it appears in the source dataset, preserving its exact form.
*   **`cleaned`**: A normalized version of the sentence (Unicode NFC normalization, extra spaces removed).
*   **`char_swap`**: The sentence with adjacent character swaps (e.g., "teh" → "the") within words to mimic typos.
*   **`random_char_insertion`**: The sentence with up to 2 random character insertions, preferring vowels near vowels for realism.
*   **`char_delete`**: The sentence with character deletions, avoiding critical positions (first/last in words).
*   **`char_substitute`**: The sentence with character substitutions, using similar-looking or keyboard-adjacent characters (e.g., 'a' → 's').
*   **`word_masking`**: The sentence with words replaced by a `BLANK` token, preferring content words (length > 3).
*   **`word_swap`**: The sentence wit adjacent word swaps (e.g., "is it" → "it is").

### Example Entry

```json
{
    "original": "Yesu Kirisita Tuura Wema TUURA WEMA",
    "cleaned": "Yesu Kirisita Tuura Wema TUURA WEMA",
    "char_swap": "Yseu Kirisita Tuura Wema TUURA WEMA",
    "random_char_insertion": "Yesu Kirisita Tuura Wema TUURA aWEMA",
    "char_delete": "Yesu Kirista Tuura Wema TURA WEMA",
    "char_substitute": "Yesu Kirisita Tuura Wema TUURA WEMs",
    "word_masking": "Yesu Kirisita BLANK Wema TUURA WEMA",
    "word_swap": "Yesu Kirisita Wema Tuura TUURA WEMA"
}
```
## Citation

If you use this dataset in your research, please cite the following paper:
code
Bibtex
```bibtex
@misc{keita2025grammaticalerrorcorrectionlowresource,
    title={Grammatical Error Correction for Low-Resource Languages: The Case of Zarma},
    author={Mamadou K. Keita and Christopher Homan and Marcos Zampieri and Adwoa Bremang and Habibatou Abdoulaye Alfari and Elysabhete Amadou Ibrahim and Dennis Owusu},
    year={2025},
    eprint={2410.15539},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2410.15539},
}
```