SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation

Junpei Zhou, Zhisong Zhang, Zecong Hu


Abstract
Quality estimation (QE) of machine translation (MT) systems is a task of growing importance. It reduces the cost of post-editing, allowing machine-translated text to be used in formal occasions. In this work, we describe our submission system in WMT 2019 sentence-level QE task. We mainly explore the utilization of pre-trained translation models in QE and adopt a bi-directional translation-like strategy. The strategy is similar to ELMo, but additionally conditions on source sentences. Experiments on WMT QE dataset show that our strategy, which makes the pre-training slightly harder, can bring improvements for QE. In WMT-2019 QE task, our system ranked in the second place on En-De NMT dataset and the third place on En-Ru NMT dataset.
Anthology ID:
W19-5411
Volume:
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–111
Language:
URL:
https://aclanthology.org/W19-5411
DOI:
10.18653/v1/W19-5411
Bibkey:
Cite (ACL):
Junpei Zhou, Zhisong Zhang, and Zecong Hu. 2019. SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 106–111, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation (Zhou et al., WMT 2019)
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PDF:
https://aclanthology.org/W19-5411.pdf