Paper Info
Title
Identifying Semantic Divergences in Parallel Text without Annotations.
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.
Year
DOI
Venue
2018
10.18653/v1/n18-1136
north american chapter of the association for computational linguistics
DocType
Volume
Citations 
Conference
abs/1803.11112
2
PageRank 
References 
Authors
0.38
0
3
Name
Order
Citations
PageRank
Yogarshi Vyas141.74
Xing Niu213510.15
Marine Carpuat358751.99