Amplexor MTExpert – machine translation adapted to the translation workflow
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Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain
Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We stud…
Machine TranslationTranslationMT-Adapted Datasheets for Datasets: Template and Repository
In this report we are taking the standardized model proposed by Gebru et al. (2018) for documenting the popular machine translation datasets of the EuroParl (Koehn, 2005) and News-Commentary (Barrault et al., 2019). With…
Machine TranslationTranslationBeyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent
Translation quality depends on purpose: the same source text demands different translations depending on audience, tone, and communicative intent. Yet MT models and metrics treat translation as a fixed mapping from sourc…
Machine TranslationCustomizing Neural Machine Translation for Subtitling
In this work, we customized a neural machine translation system for translation of subtitles in the domain of entertainment. The neural translation model was adapted to the subtitling content and style and extended by a …
Machine TranslationSegmentationSentenceTranslationCurriculum Learning for Domain Adaptation in Neural Machine Translation
We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the traini…
Domain AdaptationMachine TranslationTranslation