2018
EMNLP
EMNLP 2018
Context and Copying in Neural Machine Translation
Abstract
AbstractNeural machine translation systems with subword vocabularies are capable of translating or copying unknown words. In this work, we show that they learn to copy words based on both the context in which the words appear as well as features of the words themselves. In contexts that are particularly copy-prone, they even copy words that they have already learned they should translate. We examine the influence of context and subword features on this and other types of copying behavior.
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Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
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Keyword Pioneer
— subword vocabulary
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Hot Topic Early Bird
— context modeling
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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, Speech & Audio