UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks
This work addresses quality estimation for machine translation users, but it is incremental as it builds on previous approaches.
The paper tackled the problem of machine translation quality estimation by developing a method to mark words as OK or BAD and predict insertions, ranking first in identifying insertions for three datasets and second in others at WMT 2018.
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.