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Neural MT and Human Post-editing: a Method to Improve Editorial Quality
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In: Symposium Translation and Knowledge Transfer: News trends in the theory and practice of translation and interpreting ; https://hal.univ-rennes2.fr/hal-02495919 ; Symposium Translation and Knowledge Transfer: News trends in the theory and practice of translation and interpreting, Mar Ogea-Pozo, Carmen Expósito-Castro, Oct 2019, Cordoue, Spain (2019)
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Measuring the Impact of Neural Machine Translation on Easy-to-Read Texts: An Exploratory Study
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In: Conference on Easy-to-Read Language Research (Klaara 2019) (2019) (2019)
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Preferences of end-users for raw and post-edited NMT in a business environment
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In: ISBN: 978-2970-10957-0 ; Proceedings of the 41st Conference Translating and the Computer pp. 47-59 (2019)
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Abstract:
This paper presents an evaluation conducted with end-users of translations produced by Swiss Post's in-house Language Service. The aim is to assess whether end-users i) would rate post-edited MT more highly than raw MT; ii) would find that Swiss Post's customized NMT system produces better results than a general-purpose, off-the-shelf NMT engine (DeepL) and, lastly, when aware of translation production metadata, iii) would be willing to pay for post-edited texts. This latter aspect in particular was intended to help determine whether the customers would still value human intervention or whether they would rather accept a lower quality translation and associated risks if this means they can save on costs. Results show that the post-edited texts are preferred by the majority of the participants, even when production metadata are revealed. The in-house customized engine seems to produce better results than DeepL, since the end-users choose the raw output from our system more often than from DeepL.
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Keyword:
DeepL; End-users; info:eu-repo/classification/ddc/410.2; Language service; Neural machine translation; NMT
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URL: https://archive-ouverte.unige.ch/unige:127099
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