In this paper, we present Tilde's work on boosting the output quality and availability of Estonian machine translation systems, focusing mostly on the less resourced and morphologically complex language pairs between Estonian and Russian. We describe our efforts on collecting parallel and monolingual data for the development of better neural machine translation models, as well as experiments with various model architectures with the goal to find the best-performing model for our data. We attain state-of-the-art MT results by training a multi-way Transformer model that improves the quality by up to +3.27 BLEU points over the baseline system. We also provide a publicly available translation service via a mobile phone application.
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