Pavel Levin


Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging
Emmanouil Stergiadis | Satendra Kumar | Fedor Kovalev | Pavel Levin
Proceedings of Machine Translation Summit XVIII: Users and Providers Track

Production NMT systems typically need to serve niche domains that are not covered by adequately large and readily available parallel corpora. As a result, practitioners often fine-tune general purpose models to each of the domains their organisation caters to. The number of domains however can often become large, which in combination with the number of languages that need serving can lead to an unscalable fleet of models to be developed and maintained. We propose Multi Dimensional Tagging, a method for fine-tuning a single NMT model on several domains simultaneously, thus drastically reducing development and maintenance costs. We run experiments where a single MDT model compares favourably to a set of SOTA specialist models, even when evaluated on the domain those baselines have been fine-tuned on. Besides BLEU, we report human evaluation results. MDT models are now live at, powering an MT engine that serves millions of translations a day in over 40 different languages.


Toward a full-scale neural machine translation in production: the use case
Pavel Levin | Nishikant Dhanuka | Talaat Khalil | Fedor Kovalev | Maxim Khalilov
Proceedings of Machine Translation Summit XVI: Commercial MT Users and Translators Track