---
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.