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Varden Wang

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    Controlling Formality and Style of Machine Translation Output Using AutoML
    Aditi Viswanathan
    Antonina Kononova
    Varden HT Wang
    Information Management and Big Data (2019)
    Preview abstract An often overlooked difficulty of machine translation is producing a consistent formality (or register) in the target language. This is especially hard when the source language may have fewer levels of formality than the target language. We take a transfer learning approach using Google’s AutoML Translate to train custom neural machine translation (NMT) models to consistently produce a specific formality. We experiment with formality levels for English to Spanish, English to French and English to Czech. This approach makes it possible to have better and more consistent in-context translation while still leveraging the strength of a general purpose machine translation system. View details
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