Abstract
In recent years, machine translation research has flourished and machine translation effects have improved greatly. However, machine translation research has also encountered difficulties such as insufficient bilingual data and lack of effective feature representation, and the effect is still unsatisfactory. At the same time, as a new machine learning method, deep learning is able to learn how to abstract feature and establish a complex mapping relationship between input and output signals, which provides a new idea for statistical machine translation research. This paper uses deep neural networks to learn the key problems in statistical machine translation to better describe the representation of translation phenomena and let the performance of statistical machine translation become well. A new neural network is proposed to deal with the translation decoding process. A three-step semi-supervised training method was used to train this model. In addition, we also explored the representation of translated phrase pairs and proposed a phrase pair representation based on translation confidence. Chinese-to-English translation result show that this method can significantly improve translation.
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Liu, X. (2020). English Translation Model Design Based on Neural Network. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_32
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DOI: https://doi.org/10.1007/978-3-030-25128-4_32
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