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.
CITATION STYLE
Zhou, J., Zhang, Z., & Hu, Z. (2019). SOURCE: SOURce-conditional elmo-style model for machine translation quality estimation. In WMT 2019 - 4th Conference on Machine Translation, Proceedings of the Conference (Vol. 3, pp. 106–111). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w19-5411
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