1 research outputs found
UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks
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.Comment: 10 pages, 1 table, Proceedings of the 3rd Conference on Machine
Translation (WMT18), Brussels 31.10.2018--01.11.2018, pp. 814--82