Abstract
This paper describes our system submitted for the CCMT 2019 Quality Estimation (QE) Task, including sentence-level and word-level. We propose a new method based on predictor-estimator architecture [7] in this task. For the predictor, we adopt Transformer-DLCL [17] (dynamic linear combination of previous layers) as our feature extracting models. In order to obtain the information of translations in both directions, we use right-to-left and left-to-right two models, concatenate two feature vectors as whole quality feature vectors. For the estimator, we use a multi-layer bi-directional GRU to predict HTER scores or OK/BAD labels for different tasks. We pre-train the predictor according to machine translation (MT) method with bilingual data from WMT2019 EN-ZH task, and then jointly train predictor and estimator with the QE task data. We also construct 50K pseudo data in different methods in respond to the data scarcity. The final system integrates multiple single models to generate results.
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Acknowledgments
This work was supported in part by the National Science Foundation of China (Nos. 61876035, 61732005 and 61432013), the National Key R&D Program of China (No. 2019QY1801) and the Opening Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research. We also thank the reviewers for their insightful comments.
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Wang, Z. et al. (2019). NiuTrans Submission for CCMT19 Quality Estimation Task. In: Huang, S., Knight, K. (eds) Machine Translation. CCMT 2019. Communications in Computer and Information Science, vol 1104. Springer, Singapore. https://doi.org/10.1007/978-981-15-1721-1_9
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DOI: https://doi.org/10.1007/978-981-15-1721-1_9
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