KazParC: Kazakh Parallel Corpus for Machine Translation

TL;DR

This paper presents KazParC, the largest publicly available Kazakh parallel corpus for machine translation involving four languages, and introduces Tilmash, a neural translation model that rivals major industry systems in performance.

Contribution

The creation of KazParC as the first large-scale, publicly accessible Kazakh parallel corpus and the development of Tilmash, a neural machine translation model with competitive performance.

Findings

Tilmash achieves BLEU and chrF scores comparable to Google and Yandex Translate.

KazParC contains 371,902 parallel sentences across multiple domains.

Tilmash surpasses industry translation systems in certain evaluation metrics.

Abstract

We introduce KazParC, 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 371,902 parallel sentences covering different domains and developed with the assistance of human translators. Our research efforts also extend to the development of a neural machine translation model nicknamed Tilmash. Remarkably, the performance of Tilmash is on par with, and in certain instances, surpasses that of industry giants, such as Google Translate and Yandex Translate, as measured by standard evaluation metrics, such as BLEU and chrF. Both KazParC and Tilmash are openly available for download under the Creative Commons Attribution 4.0 International License (CC BY 4.0) through our GitHub repository.