The use of neural machine translation (NMT) in a professional scenario implies a number of challenges despite growing evidence that, in language combinations such as English to Spanish, NMT output quality has already outperformed statistical machine translation in terms of automatic metrics scores. This article presents the result of an empirical test that aims to shed light on the differences between NMT postediting and translation with the aid of a translation memory (TM). The results show that NMT post-editing involves less editing than TM segments, but this editing appears to take more time, with the consequence that NMT post-editing does not seem to improve productivity as may have been expected. This might be due to the fact that NMT segments show a higher variability in terms of quality and time invested in post-editing than TM segments that are 'more similar' on average. Finally, results show that translators who perceive that NMT boosts their productivity actually performed faster than those who perceive that NMT slows them down.
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The aim of the MultiTraiNMT Erasmus+ project is to develop an open innovative syllabus in neural machine translation (NMT) for language learners and translators as multilingual citizens. Machine translation is seen as a resource to provide support to citizens when trying to acquire and develop language skills, provided they are given informed and critical training. Machine translation would thus help tackle the mismatch between the EU aim of having multilingual citizens who speak at least two foreign languages and the current situation in which they generally fall far short of this objective. The training materials consist of an open-access coursebook, an open-source NMT web application (MutNMT) for training purposes and corresponding activities.
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