2017
EMNLP
EMNLP 2017
A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output
Abstract
AbstractStylistic variations of language, such as formality, carry speakers’ intention beyond literal meaning and should be conveyed adequately in translation. We propose to use lexical formality models to control the formality level of machine translation output. We demonstrate the effectiveness of our approach in empirical evaluations, as measured by automatic metrics and human assessments.
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Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
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Keyword Pioneer
— formality control
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Hot Topic Early Bird
— style transfer
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio