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
Venues:
ACL | WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–111
Language:
URL:
https://www.aclweb.org/anthology/W19-5411
DOI:
10.18653/v1/W19-5411
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-5411.pdf