A Large-Scale Study of Machine Translation in the Turkic Languages

TL;DR

This paper conducts a comprehensive large-scale analysis of neural machine translation for Turkic languages, addressing data scarcity and providing resources, baselines, and evaluations to advance translation quality in these languages.

Contribution

It introduces a large parallel corpus, bilingual baselines, new test sets, and human evaluation for Turkic languages, facilitating future NMT research and development.

Findings

Identified bottlenecks in Turkic language NMT systems.

Provided extensive datasets and benchmarks for 26 language pairs.

Released resources publicly to support further research.

Abstract

Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 2 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.