metadata

task_categories:
  - translation
language:
  - kk
  - en
  - ru
  - tr
pretty_name: Kazakh Parallel Corpus
dataset_info:
  - config_name: kazparc_raw
    features:
      - name: id
        dtype: string
      - name: kk
        dtype: string
      - name: en
        dtype: string
      - name: ru
        dtype: string
      - name: tr
        dtype: string
      - name: domain
        dtype: string
    splits:
      - name: train
        num_bytes: 230957871
        num_examples: 371902
  - config_name: kazparc
    features:
      - name: id
        dtype: string
      - name: source_lang
        dtype: string
      - name: target_lang
        dtype: string
      - name: domain
        dtype: string
      - name: pair
        dtype: string
    splits:
      - name: train
        num_bytes: 584249013
        num_examples: 1742956
      - name: validation
        num_bytes: 145898177
        num_examples: 435742
      - name: test
        num_bytes: 8936796
        num_examples: 28500
  - config_name: sync_raw
    features:
      - name: id
        dtype: string
      - name: kk
        dtype: string
      - name: en
        dtype: string
      - name: ru
        dtype: string
      - name: tr
        dtype: string
      - name: domain
        dtype: string
    splits:
      - name: train
        num_bytes: 1278185141
        num_examples: 1797066
  - config_name: sync
    features:
      - name: id
        dtype: string
      - name: source_lang
        dtype: string
      - name: target_lang
        dtype: string
      - name: domain
        dtype: string
      - name: pair
        dtype: string
    splits:
      - name: train
        num_bytes: 3654616080
        num_examples: 9654322
      - name: validation
        num_bytes: 405929897
        num_examples: 1072705
configs:
  - config_name: kazparc_raw
    data_files:
      - split: train
        path: kazparc/01_kazparc_all_entries.csv
    default: true
  - config_name: kazparc
    data_files:
      - split: train
        path:
          - kazparc/02_kazparc_train_kk_en.csv
          - kazparc/03_kazparc_train_kk_ru.csv
          - kazparc/04_kazparc_train_kk_tr.csv
          - kazparc/05_kazparc_train_en_ru.csv
          - kazparc/06_kazparc_train_en_tr.csv
          - kazparc/07_kazparc_train_ru_tr.csv
      - split: validation
        path:
          - kazparc/08_kazparc_valid_kk_en.csv
          - kazparc/09_kazparc_valid_kk_ru.csv
          - kazparc/10_kazparc_valid_kk_tr.csv
          - kazparc/11_kazparc_valid_en_ru.csv
          - kazparc/12_kazparc_valid_en_tr.csv
          - kazparc/13_kazparc_valid_ru_tr.csv
      - split: test
        path:
          - kazparc/14_kazparc_test_kk_en.csv
          - kazparc/15_kazparc_test_kk_ru.csv
          - kazparc/16_kazparc_test_kk_tr.csv
          - kazparc/17_kazparc_test_en_ru.csv
          - kazparc/18_kazparc_test_en_tr.csv
          - kazparc/19_kazparc_test_ru_tr.csv
  - config_name: sync_raw
    data_files:
      - split: train
        path: sync/20_sync_all_entries.csv
  - config_name: sync
    data_files:
      - split: train
        path:
          - sync/21_sync_train_kk_en.csv
          - sync/22_sync_train_kk_ru.csv
          - sync/23_sync_train_kk_tr.csv
          - sync/24_sync_train_en_ru.csv
          - sync/25_sync_train_en_tr.csv
          - sync/26_sync_train_ru_tr.csv
      - split: validation
        path:
          - sync/27_sync_valid_kk_en.csv
          - sync/28_sync_valid_kk_ru.csv
          - sync/29_sync_valid_kk_tr.csv
          - sync/30_sync_valid_en_ru.csv
          - sync/31_sync_valid_en_tr.csv
          - sync/32_sync_valid_ru_tr.csv
size_categories:
  - 100K<n<1M

Dataset Description

Repository: https://github.com/IS2AI/KazParC
Paper: https://arxiv.org/abs/2403.19399

KazParC

Kazakh Parallel Corpus (KazParC) is a parallel corpus designed for machine translation across Kazakh, English, Russian, and Turkish. The first and largest publicly available corpus of its kind, KazParC contains a collection of 372,164 parallel sentences covering different domains and developed with the assistance of human translators.

Data Sources and Domains

The data sources include

proverbs and sayings
terminology glossaries
phrasebooks
literary works
periodicals
language learning materials, including the SCoRE corpus by Chujo et al. (2015)
educational video subtitle collections, such as QED by Abdelali et al. (2014)
news items, such as KazNERD (Yeshpanov et al., 2022) and WMT (Tiedemann, 2012)
TED talks
governmental and regulatory legal documents from Kazakhstan
communications from the official website of the President of the Republic of Kazakhstan
United Nations publications
image captions from sources like COCO

The sources are categorised into five broad domains:

Domain	lines		tokens
	lines		EN		KK		RU		TR
	#	%	#	%	#	%	#	%	#	%
Mass media	120,547	32.4	1,817,276	28.3	1,340,346	28.6	1,454,430	29.0	1,311,985	28.5
General	94,988	25.5	844,541	13.1	578,236	12.3	618,960	12.3	608,020	13.2
Legal documents	77,183	20.8	2,650,626	41.3	1,925,561	41.0	1,991,222	39.7	1,880,081	40.8
Education and science	46,252	12.4	522,830	8.1	392,348	8.4	444,786	8.9	376,484	8.2
Fiction	32,932	8.9	589,001	9.2	456,385	9.7	510,168	10.2	433,968	9.4
Total	371,902	100	6,424,274	100	4,692,876	100	5,019,566	100	4,610,538	100

Pair	# lines	# sents	# tokens	# types
KK↔EN	363,594	362,230 361,087	4,670,789 6,393,381	184,258 59,062
KK↔RU	363,482	362,230 362,748	4,670,593 4,996,031	184,258 183,204
KK↔TR	362,150	362,230 361,660	4,668,852 4,586,421	184,258 175,145
EN↔RU	363,456	361,087 362,748	6,392,301 4,994,310	59,062 183,204
EN↔TR	362,392	361,087 361,660	6,380,703 4,579,375	59,062 175,145
RU↔TR	363,324	362,748 361,660	4,999,850 4,591,847	183,204 175,145

Synthetic Corpus

To make our parallel corpus more extensive, we carried out web crawling to gather a total of 1,797,066 sentences from English-language websites. These sentences were then automatically translated into Kazakh, Russian, and Turkish using the Google Translate service. We refer to this collection of data as 'SynC' (Synthetic Corpus).

Pair	# lines	# sents	# tokens	# types
KK↔EN	1,787,050	1,782,192 1,781,019	26,630,960 35,291,705	685,135 300,556
KK↔RU	1,787,448	1,782,192 1,777,500	26,654,195 30,241,895	685,135 672,146
KK↔TR	1,791,425	1,782,192 1,782,257	26,726,439 27,865,860	685,135 656,294
EN↔RU	1,784,513	1,781,019 1,777,500	35,244,800 30,175,611	300,556 672,146
EN↔TR	1,788,564	1,781,019 1,782,257	35,344,188 27,806,708	300,556 656,294
RU↔TR	1,788,027	1,777,500 1,782,257	30,269,083 27,816,210	672,146 656,294

Data Splits

KazParC

We first created a test set by randomly selecting 250 unique and non-repeating rows from each of the sources outlined in Data Sources and Domains. The remaining data were divided into language pairs, following an 80/20 split, while ensuring that the distribution of domains was maintained within both the training and validation sets.

Pair	Train				Valid				Test
	# lines	# sents	# tokens	# types	# lines	# sents	# tokens	# lines	# lines	# sents	# tokens	# lines
	KK↔EN	290,877	286,958 286,197	3,693,263 5,057,687	164,766 54,311	72,719	72,426 72,403	920,482 1,259,827	83,057 32,063	4,750	4,750 4,750	57,044 75,867	17,475 9,729
KK↔RU	290,785	286,943 287,215	3,689,799 3,945,741	164,995 165,882	72,697	72,413 72,439	923,750 988,374	82,958 87,519	4,750	4,750 4,750	57,044 61,916	17,475 18,804
KK↔TR	289,720	286,694 286,279	3,691,751 3,626,361	164,961 157,460	72,430	72,211 72,190	920,057 904,199	82,698 80,885	4,750	4,750 4,750	57,044 55,861	17,475 17,284
EN↔RU	290,764	286,185 287,261	5,058,530 3,950,362	54,322 165,701	72,692	72,377 72,427	1,257,904 982,032	32,208 87,541	4,750	4,750 4,750	75,867 61,916	9,729 18,804
EN↔TR	289,913	285,967 286,288	5,048,274 3,621,531	54,224 157,369	72,479	72,220 72,219	1,256,562 901,983	32,269 80,838	4,750	4,750 4,750	75,867 55,861	9,729 17,284
RU↔TR	290,899	287,241 286,475	3,947,809 3,626,436	165,482 157,470	72,725	72,455 72,362	990,125 909,550	87,831 80,962	4,750	4,750 4,750	61,916 55,861	18,804 17,284

SynC

We divided the synthetic corpus into training and validation sets with a 90/10 ratio.

Pair	Train				Valid
Pair	# lines	# sents	# tokens	# types	# lines	# sents	# tokens	# types
KK↔EN	1,608,345	1,604,414 1,603,426	23,970,260 31,767,617	650,144 286,372	178,705	178,654 178,639	2,660,700 3,524,088	208,838 105,517
KK↔RU	1,608,703	1,604,468 1,600,643	23,992,148 27,221,583	650,170 642,604	178,745	178,691 178,642	2,662,047 3,020,312	209,188 235,642
KK↔TR	1,612,282	1,604,793 1,604,822	24,053,671 25,078,688	650,384 626,724	179,143	179,057 179,057	2,672,768 2,787,172	209,549 221,773
EN↔RU	1,606,061	1,603,199 1,600,372	31,719,781 27,158,101	286,645 642,686	178,452	178,419 178,379	3,525,019 3,017,510	104,834 235,069
EN↔TR	1,609,707	1,603,636 1,604,545	31,805,393 25,022,782	286,387 626,740	178,857	178,775 178,796	3,538,795 2,783,926	105,641 221,372
RU↔TR	1,609,224	1,600,605 1,604,521	27,243,278 25,035,274	642,797 626,587	178,803	178,695 178,750	3,025,805 2,780,936	235,970 221,792

Corpus Structure

The entire corpus is organised into two distinct groups based on their file prefixes. Files "01" through "19" have the "kazparc" prefix, while Files "20" to "32" have the "sync" prefix.

├── kazparc
   ├── 01_kazparc_all_entries.csv
   ├── 02_kazparc_train_kk_en.csv
   ├── 03_kazparc_train_kk_ru.csv
   ├── 04_kazparc_train_kk_tr.csv
   ├── 05_kazparc_train_en_ru.csv
   ├── 06_kazparc_train_en_tr.csv
   ├── 07_kazparc_train_ru_tr.csv
   ├── 08_kazparc_valid_kk_en.csv
   ├── 09_kazparc_valid_kk_ru.csv
   ├── 10_kazparc_valid_kk_tr.csv
   ├── 11_kazparc_valid_en_ru.csv
   ├── 12_kazparc_valid_en_tr.csv
   ├── 13_kazparc_valid_ru_tr.csv
   ├── 14_kazparc_test_kk_en.csv
   ├── 15_kazparc_test_kk_ru.csv
   ├── 16_kazparc_test_kk_tr.csv
   ├── 17_kazparc_test_en_ru.csv
   ├── 18_kazparc_test_en_tr.csv
   ├── 19_kazparc_test_ru_tr.csv
├── sync
   ├── 20_sync_all_entries.csv
   ├── 21_sync_train_kk_en.csv
   ├── 22_sync_train_kk_ru.csv
   ├── 23_sync_train_kk_tr.csv
   ├── 24_sync_train_en_ru.csv
   ├── 25_sync_train_en_tr.csv
   ├── 26_sync_train_ru_tr.csv
   ├── 27_sync_valid_kk_en.csv
   ├── 28_sync_valid_kk_ru.csv
   ├── 29_sync_valid_kk_tr.csv
   ├── 30_sync_valid_en_ru.csv
   ├── 31_sync_valid_en_tr.csv
   ├── 32_sync_valid_ru_tr.csv

KazParC files

File "01" contains the original, unprocessed text data for the four languages considered within KazParC.
Files "02" through "19" represent pre-processed texts divided into language pairs for training (Files "02" to "07"), validation (Files "08" to "13"), and testing (Files "14" to "19"). Language pairs are indicated within the filenames using two-letter language codes (e.g., kk_en).

SynC files

File "20" contains raw, unprocessed text data for the four languages.
Files "21" to "32" contain pre-processed text divided into language pairs for training (Files "21" to "26") and validation (Files "27" to "32") purposes.

Data Fields

In both "01" and "20", each line consists of specific components:

id: the unique line identifier
kk: the sentence in Kazakh
en: the sentence in English
ru: the sentence in Russian
tr: the sentence in Turkish
domain: the domain of the sentence

For the other files, the fields are:

id: the unique line identifier
source_lang: the source language code
target_lang: the target language code
domain: the domain of the sentence
pair: the language pair

How to Use

To load the subsets of KazParC separately:

from datasets import load_dataset

kazparc_raw = load_dataset("issai/kazparc", "kazparc_raw")
kazparc = load_dataset("issai/kazparc", "kazparc")
sync_raw = load_dataset("issai/kazparc", "sync_raw")
sync = load_dataset("issai/kazparc", "sync")