@inproceedings{sanchez-martinez-etal-2018-ualacant,
title = "{UA}lacant machine translation quality estimation at {WMT} 2018: a simple approach using phrase tables and feed-forward neural networks",
author = "S{\'a}nchez-Mart{\'\i}nez, Felipe and
Espl{\`a}-Gomis, Miquel and
Forcada, Mikel L.",
booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6464",
doi = "10.18653/v1/W18-6464",
pages = "801--808",
abstract = "We describe the Universitat d{'}Alacant submissions to the word- and sentence-level machine translation (MT) quality estimation (QE) shared task at WMT 2018. Our approach to word-level MT QE builds on previous work to mark the words in the machine-translated sentence as \textit{OK} or \textit{BAD}, and is extended to determine if a word or sequence of words need to be inserted in the gap after each word. Our sentence-level submission simply uses the edit operations predicted by the word-level approach to approximate TER. The method presented ranked first in the sub-task of identifying insertions in gaps for three out of the six datasets, and second in the rest of them.",
}
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%0 Conference Proceedings
%T UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks
%A Sánchez-Martínez, Felipe
%A Esplà-Gomis, Miquel
%A Forcada, Mikel L.
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 oct
%I Association for Computational Linguistics
%C Belgium, Brussels
%F sanchez-martinez-etal-2018-ualacant
%X We describe the Universitat d’Alacant submissions to the word- and sentence-level machine translation (MT) quality estimation (QE) shared task at WMT 2018. Our approach to word-level MT QE builds on previous work to mark the words in the machine-translated sentence as OK or BAD, and is extended to determine if a word or sequence of words need to be inserted in the gap after each word. Our sentence-level submission simply uses the edit operations predicted by the word-level approach to approximate TER. The method presented ranked first in the sub-task of identifying insertions in gaps for three out of the six datasets, and second in the rest of them.
%R 10.18653/v1/W18-6464
%U https://aclanthology.org/W18-6464
%U https://doi.org/10.18653/v1/W18-6464
%P 801-808
Markdown (Informal)
[UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks](https://aclanthology.org/W18-6464) (Sánchez-Martínez et al., 2018)
ACL