@inproceedings{vyas-etal-2018-identifying,
title = "Identifying Semantic Divergences in Parallel Text without Annotations",
author = "Vyas, Yogarshi and
Niu, Xing and
Carpuat, Marine",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1136",
doi = "10.18653/v1/N18-1136",
pages = "1503--1515",
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.",
}
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%0 Conference Proceedings
%T Identifying Semantic Divergences in Parallel Text without Annotations
%A Vyas, Yogarshi
%A Niu, Xing
%A Carpuat, Marine
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F vyas-etal-2018-identifying
%X 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.
%R 10.18653/v1/N18-1136
%U https://aclanthology.org/N18-1136
%U https://doi.org/10.18653/v1/N18-1136
%P 1503-1515
Markdown (Informal)
[Identifying Semantic Divergences in Parallel Text without Annotations](https://aclanthology.org/N18-1136) (Vyas et al., NAACL 2018)
ACL
- Yogarshi Vyas, Xing Niu, and Marine Carpuat. 2018. Identifying Semantic Divergences in Parallel Text without Annotations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1503–1515, New Orleans, Louisiana. Association for Computational Linguistics.