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arXiv · 1811.02510

UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks

Miquel Esplà-Gomis·Felipe Sánchez-Martínez·Mikel L. Forcada
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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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Miquel Esplà-Gomis, Felipe Sánchez-Martínez, Mikel L. Forcada. 2018-11-06. UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks. https://arxiv.org/abs/1811.02510

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