2017
EMNLP
EMNLP 2017
Machine Translation, it’s a question of style, innit? The case of English tag questions
Abstract
AbstractIn this paper, we address the problem of generating English tag questions (TQs) (e.g. it is, isn’t it?) in Machine Translation (MT). We propose a post-edition solution, formulating the problem as a multi-class classification task. We present (i) the automatic annotation of English TQs in a parallel corpus of subtitles and (ii) an approach using a series of classifiers to predict TQ forms, which we use to post-edit state-of-the-art MT outputs. Our method provides significant improvements in English TQ translation when translating from Czech, French and German, in turn improving the fluidity, naturalness, grammatical correctness and pragmatic coherence of MT output.
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The Questioner
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Keyword Pioneer
— english language
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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