  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.
