  Little research has been done on Neural Machine Translation (NMT) for
Azerbaijani. In this paper, we benchmark the performance of Azerbaijani-English
NMT systems on a range of techniques and datasets. We evaluate which
segmentation techniques work best on Azerbaijani translation and benchmark the
performance of Azerbaijani NMT models across several domains of text. Our
results show that while Unigram segmentation improves NMT performance and
Azerbaijani translation models scale better with dataset quality than quantity,
cross-domain generalization remains a challenge
