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SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task.

, , , , and . WAT@EMNLP-IJCNLP, page 189-194. Association for Computational Linguistics, (2019)

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Priming Neural Machine Translation., , , , and . WMT@EMNLP, page 516-527. Association for Computational Linguistics, (2020)Neural Network Architectures for Arabic Dialect Identification., , , and . VarDial@COLING 2018, page 128-136. Association for Computational Linguistics, (2018)Boosting Neural Machine Translation., , , and . IJCNLP(2), page 271-276. Asian Federation of Natural Language Processing, (2017)OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU., , , , , , and . NMT@ACL, page 122-128. Association for Computational Linguistics, (2018)Selective addition of corpus-extracted phrasal lexical rules to a rule-based machine translation system., , and . MTSummit, (2009)Integrating Domain Terminology into Neural Machine Translation., , and . COLING, page 3925-3937. International Committee on Computational Linguistics, (2020)OpenNMT: Open-Source Toolkit for Neural Machine Translation., , , , and . ACL (System Demonstrations), page 67-72. Association for Computational Linguistics, (2017)Convergence of Translation Memory and Statistical Machine Translation, and . Proceedings of AMTA Workshop on MT Research and the Translation Industry, page 21--31. Denver, (2010)SYSTRAN @ WNGT 2019: DGT Task., , and . NGT@EMNLP-IJCNLP, page 262-267. Association for Computational Linguistics, (2019)Fixing Translation Divergences in Parallel Corpora for Neural MT., , , and . EMNLP, page 2967-2973. Association for Computational Linguistics, (2018)