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When the How Outweighs the What: the Pivotal Importance of Context., , , , , , and . BIBM, page 2149-2156. IEEE, (2019)An Empirical Exploration of Curriculum Learning for Neural Machine Translation., , , , , , , , and . CoRR, (2018)Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection., , , , and . CoRR, (2023)MoJo: Bringing Hybrid MT to the Center for Applied Machine Translation.. AMTA (2), page 654-714. The Association for Machine Translation in the Americas, (2016)Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MT., and . AMTA (1), page 13-25. Association for Machine Translation in the Americas, (2018)Can Statistical Post-Editing with a Small Parallel Corpus Save a Weak MT Engine?. LREC, page 2138-2142. European Language Resources Association (ELRA), (2012)Class-based N-gram language difference models for data selection., , , and . IWSLT, (2015)A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output., , and . EMNLP, page 2814-2819. Association for Computational Linguistics, (2017)Identifying Fluently Inadequate Output in Neural and Statistical Machine Translation., , , and . MTSummit (1), page 233-243. European Association for Machine Translation, (2019)An Exploration of Placeholding in Neural Machine Translation., , , and . MTSummit (1), page 182-192. European Association for Machine Translation, (2019)