SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation
Junpei Zhou, Zhisong Zhang, Zecong Hu · 2019
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.