UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks
Felipe Sánchez-Martínez, Miquel Esplà-Gomis, Mikel L. Forcada · 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 OK or 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.