Google Research

Controlling Formality and Style of Machine Translation Output Using AutoML

Information Management and Big Data (2019) (to appear)

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.

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