2018
NAACL
NAACL 2018
Identifying Semantic Divergences in Parallel Text without Annotations
Abstract
AbstractRecognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation. We show that our semantic model detects divergences more accurately than models based on surface features derived from word alignments, and that these divergences matter for neural machine translation.
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Interdisciplinary Bridge
— Interdisciplinary and Natural Language Processing
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Keyword Pioneer
— semantic divergence
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Hot Topic Early Bird
— word alignment
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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, Security & Privacy, Speech & Audio