Identifying Semantic Divergences in Parallel Text without Annotations

ACL 2018

Identifying Semantic Divergences in Parallel Text without Annotations

Jan 21, 2021
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Abstract: Recognizing 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. Authors: Yogarshi Vyas, Xing Niu, Marine Carpuat (University of Maryland)

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