Abstract
The goal of China Conference on Machine Translation (CCMT 2019) Shared Task on Quality Estimation (QE) is to investigate automatic methods for estimating the quality of \( {\text{Chinese}}\!\leftrightarrow\!{\text{English}} \) machine translation results without reference translations. This paper presents the submissions of our team for the sentence-level Quality Estimation shared task of CCMT19. Considering the good performance of neural models in previous shared tasks of WMT, our submissions also include two neural-based models: one is Bi-Transformer which proposes the model as a feature extractor with a bidirectional transformer and then processes the semantic representations of source and the translation output with a Bi-LSTM predictive model for automatic quality estimation, and the other BiRNN architecture uses only two bi-directional RNNs (bi-RNN) with Gated Recurrent Units (GRUs) as encoders, and learns representation of the source and translation sentence pairs to predict the quality of translation outputs.
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Acknowledgment
This work is supported by China Postdoctoral Science Foundation (CPSF, Grant No. 2018M640069).
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Zhang, Y., Feng, C., Li, H. (2019). Quality Estimation with Transformer and RNN Architectures. 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_7
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DOI: https://doi.org/10.1007/978-981-15-1721-1_7
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