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We describe the Universitat d'Alacant submissions to the word- andsentence-level machine translation (MT) quality estimation (QE) shared task atWMT 2018. Our approach to word-level MT QE builds on previous work to mark thewords in the machine-translated sentence as \textit{OK} or \textit{BAD}, and isextended to determine if a word or sequence of words need to be inserted in thegap after each word. Our sentence-level submission simply uses the editoperations predicted by the word-level approach to approximate TER. The methodpresented ranked first in the sub-task of identifying insertions in gaps forthree out of the six datasets, and second in the rest of them.

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