  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.
