@inproceedings{toral-sanchez-cartagena-2017-multifaceted,
title = "A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions",
author = "Toral, Antonio and
S{\'a}nchez-Cartagena, V{\'\i}ctor M.",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1100",
pages = "1063--1073",
abstract = "We aim to shed light on the strengths and weaknesses of the newly introduced neural machine translation paradigm. To that end, we conduct a multifaceted evaluation in which we compare outputs produced by state-of-the-art neural machine translation and phrase-based machine translation systems for 9 language directions across a number of dimensions. Specifically, we measure the similarity of the outputs, their fluency and amount of reordering, the effect of sentence length and performance across different error categories. We find out that translations produced by neural machine translation systems are considerably different, more fluent and more accurate in terms of word order compared to those produced by phrase-based systems. Neural machine translation systems are also more accurate at producing inflected forms, but they perform poorly when translating very long sentences.",
}
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%0 Conference Proceedings
%T A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions
%A Toral, Antonio
%A Sánchez-Cartagena, Víctor M.
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F toral-sanchez-cartagena-2017-multifaceted
%X We aim to shed light on the strengths and weaknesses of the newly introduced neural machine translation paradigm. To that end, we conduct a multifaceted evaluation in which we compare outputs produced by state-of-the-art neural machine translation and phrase-based machine translation systems for 9 language directions across a number of dimensions. Specifically, we measure the similarity of the outputs, their fluency and amount of reordering, the effect of sentence length and performance across different error categories. We find out that translations produced by neural machine translation systems are considerably different, more fluent and more accurate in terms of word order compared to those produced by phrase-based systems. Neural machine translation systems are also more accurate at producing inflected forms, but they perform poorly when translating very long sentences.
%U https://aclanthology.org/E17-1100
%P 1063-1073
Markdown (Informal)
[A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions](https://aclanthology.org/E17-1100) (Toral & Sánchez-Cartagena, EACL 2017)
ACL