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


Abstract
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.
Anthology ID:
W18-6464
Volume:
Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Month:
October
Year:
2018
Address:
Belgium, Brussels
Venues:
EMNLP | WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
801–808
Language:
URL:
https://www.aclweb.org/anthology/W18-6464
DOI:
10.18653/v1/W18-6464
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6464.pdf