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        <title>Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation</title>
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            <title>Proceedings of the First Workshop on Neural Machine Translation</title>
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            <namePart type="given">Alexandra</namePart>
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    <abstract>Parallel corpora are often not as parallel as one might assume: non-literal translations and noisy translations abound, even in curated corpora routinely used for training and evaluation. We use a cross-lingual textual entailment system to distinguish sentence pairs that are parallel in meaning from those that are not, and show that filtering out divergent examples from training improves translation quality.</abstract>
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%0 Conference Proceedings
%T Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation
%A Carpuat, Marine
%A Vyas, Yogarshi
%A Niu, Xing
%Y Luong, Thang
%Y Birch, Alexandra
%Y Neubig, Graham
%Y Finch, Andrew
%S Proceedings of the First Workshop on Neural Machine Translation
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver
%F carpuat-etal-2017-detecting
%X Parallel corpora are often not as parallel as one might assume: non-literal translations and noisy translations abound, even in curated corpora routinely used for training and evaluation. We use a cross-lingual textual entailment system to distinguish sentence pairs that are parallel in meaning from those that are not, and show that filtering out divergent examples from training improves translation quality.
%R 10.18653/v1/W17-3209
%U https://aclanthology.org/W17-3209
%U https://doi.org/10.18653/v1/W17-3209
%P 69-79

Markdown (Informal)

[Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation](https://aclanthology.org/W17-3209) (Carpuat et al., NGT 2017)

• Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation (Carpuat et al., NGT 2017)

ACL

• Marine Carpuat, Yogarshi Vyas, and Xing Niu. 2017. Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation. In Proceedings of the First Workshop on Neural Machine Translation, pages 69–79, Vancouver. Association for Computational Linguistics.