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
Editors:
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
801–808
Language:
URL:
https://aclanthology.org/W18-6464
DOI:
10.18653/v1/W18-6464
Bibkey:
Cite (ACL):
Felipe Sánchez-Martínez, Miquel Esplà-Gomis, and Mikel L. Forcada. 2018. UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 801–808, Belgium, Brussels. Association for Computational Linguistics.
Cite (Informal):
UAlacant machine translation quality estimation at WMT 2018: a simple approach using phrase tables and feed-forward neural networks (Sánchez-Martínez et al., WMT 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6464.pdf