Abstract
Translation quality estimation (QE) has been attracting increasing attention due to its potential to reduce post-editing human effort. However, QE still suffers heavily from the problem that the quality annotation data remain expensive and small. In this paper, we focus on overcoming the limitation of QE data and explore to utilize the high level latent features learned by the pre-trained language models to reduce the model’s dependence on QE data and improve QE performance. Specifically, we propose two strategies to integrate the pre-trained language features into QE model: (1) a mixed integration model, where the pre-trained language features are fed into the QE mode combined with other features; and (2) a constrained integration model, where a constraint mechanism is used to adjust the reporting bias of our first integration model and enhance the robustness of the QE model. Experimental results on WMT17 QE task demonstrate the effectiveness of our approaches.
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Acknowledgements
This work is supported by the National Nature Science Foundation of China (Nos. 61370130, 61976015, 61473294 and 61876198), the Fundamental Research Funds for the Central Universities (2015JBM033), the International Science and Technology Cooperation Program of China under grant No. K11F100010, the Fundamental Research Funds for the Central Universities (No. 2018YJS043), Major Projects of Fundamental Research on Philosophy and Social Sciences of Henan Education Department (2016-JCZD-022), and Toshiba (China) Co., Ltd.
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Miao, G., Di, H., Xu, J., Yang, Z., Chen, Y., Ouchi, K. (2019). Improving Quality Estimation of Machine Translation by Using Pre-trained Language Representation. 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_2
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DOI: https://doi.org/10.1007/978-981-15-1721-1_2
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