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---
dataset_info:
features:
- name: src_lang
dtype: string
- name: src_sent
dtype: string
- name: tgt_lang
dtype: string
- name: tgt_sent
dtype: string
splits:
- name: kaa_eng
num_bytes: 19047157
num_examples: 100000
- name: kaa_rus
num_bytes: 27731049
num_examples: 100000
- name: kaa_uzb
num_bytes: 30608474
num_examples: 100000
download_size: 46148914
dataset_size: 77386680
configs:
- config_name: default
data_files:
- split: kaa_eng
path: data/kaa_eng-*
- split: kaa_rus
path: data/kaa_rus-*
- split: kaa_uzb
path: data/kaa_uzb-*
language:
- en
- ru
- uz
- kaa
pretty_name: dilmash
size_categories:
- 100K<n<1M
license: mit
task_categories:
- translation
tags:
- dilmash
- karakalpak
---
# Dilmash: Karakalpak Parallel Corpus
This repository contains a parallel corpus for the Karakalpak language, developed as part of the research paper "Open Language Data Initiative: Advancing Low-Resource Machine Translation for Karakalpak".
## Dataset Description
The Karakalpak Parallel Corpus is a collection of 300,000 sentence pairs, designed to support machine translation tasks involving the Karakalpak language. It includes:
- Uzbek-Karakalpak (100,000 pairs)
- Russian-Karakalpak (100,000 pairs)
- English-Karakalpak (100,000 pairs)
## Usage
This dataset is intended for training and evaluating machine translation models involving the Karakalpak language.
To load and use dataset, run this script:
```python
from datasets import load_dataset
dilmash_corpus = load_dataset("tahrirchi/dilmash")
```
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 77.4 MB
- **Size of the generated dataset:** 46.1 MB
- **Total amount of disk used:** 123.5 MB
An example of 'kaa_eng' looks as follows.
```
{'src_lang': 'kaa_Latn',
'src_sent': 'Pedagogikalıq ideal balaǵa ıktıyatlılıq penen katnasta bolıw principine bárqulla, úlken hám kishi jumıslarda súyeniwdi talan etedi.',
'tgt_lang': 'eng_Latn',
'tgt_sent': 'The ideal of education demands that the principle of treating children with care be observed at all times, in both big and small matters.'
}
```
### Data Fields
The data fields are the same among all splits.
- `src_lang`: a `string` feature that contains source language.
- `src_sent`: a `string` feature that contains sentence in source language.
- `tgt_lang`: a `string` feature that contains target language.
- `tgt_sent`: a `string` feature that contains sentence in target language.
### Data Splits
| split_name |num_examples|
|-----------------|-----------:|
| kaa_eng | 100000 |
| kaa_rus | 100000 |
| kaa_uzb | 100000 |
## Data Sources
The corpus comprises diverse parallel texts sourced from multiple domains:
- 23% sentences from news sources
- 34% sentences from books (novels, non-fiction)
- 24% sentences from bilingual dictionaries
- 19% sentences from school textbooks
Additionally, 4,000 English-Karakalpak pairs were sourced from the Gatitos Project (Jones et al., 2023)[https://aclanthology.org/2023.emnlp-main.26].
## Data Preparation
The data mining process involved local mining techniques, ensuring that parallel sentences were extracted from translations of the same book, document, or article. Sentence alignment was performed using LaBSE (Language-agnostic BERT Sentence Embedding) embeddings. For more information, plase refet to [our paper](https://arxiv.org/abs/2409.04269).
## Citation
If you use this dataset in your research, please cite our paper:
```bibtex
@misc{mamasaidov2024openlanguagedatainitiative,
title={Open Language Data Initiative: Advancing Low-Resource Machine Translation for Karakalpak},
author={Mukhammadsaid Mamasaidov and Abror Shopulatov},
year={2024},
eprint={2409.04269},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2409.04269},
}
```
## Gratitude
We are thankful to these awesome organizations and people for helping to make it happen:
- [David Dalé](https://daviddale.ru): for advise throughout the process
- Perizad Najimova: for expertise and assistance with the Karakalpak language
- [Nurlan Pirjanov](https://www.linkedin.com/in/nurlan-pirjanov/): for expertise and assistance with the Karakalpak language
- [Atabek Murtazaev](https://www.linkedin.com/in/atabek/): for advise throughout the process
- Ajiniyaz Nurniyazov: for advise throughout the process
We would also like to express our sincere appreciation to [Google for Startups](https://cloud.google.com/startup) for generously sponsoring the compute resources necessary for our experiments. Their support has been instrumental in advancing our research in low-resource language machine translation.
## Contacts
We believe that this work will enable and inspire all enthusiasts around the world to open the hidden beauty of low-resource languages, in particular Karakalpak.
For further development and issues about the dataset, please use m.mamasaidov@tahrirchi.uz or a.shopolatov@tahrirchi.uz to contact. |