Machine Automatic Translation Quality Evaluation Model Based on Recurrent Neural Network Algorithm

Author:

Ren Beibei

Publisher

Springer International Publishing

Reference12 articles.

1. Mason, S.B., Moorkens, J., Castilho, S., Gaspari, F., Doherty, S. (eds.): Translation quality assessment: from principles to practice. Mach. Transl. 33(3), 269–277 (2019)

2. Li, H., Huang, G., Cai, D., et al.: Neural machine translation with noisy lexical constraints. IEEE/ACM Trans. Audio, Speech Lang. Process. 8(99), 1–5 (2020)

3. Ren, Q., Su, Y., Wu, N.: Research on Mongolian-Chinese machine translation based on the end-to-end neural network. Int. J. Wavelets Multiresolut. Inf. Process. 18(1), 46–59 (2020)

4. Screen, B.: Productivity and quality when editing machine translation and translation memory outputs: an empirical analysis of English to Welsh translation. Studia Celtica Posnaniensia 2(1), 1–24 (2017)

5. Kim, H., Jung, H.Y., Kwon, H., et al.: Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Trans. Asian Low-Resource Lang. Inf. Process. 17(1), 1–22 (2017)

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1. Quality Evaluation Model of Automatic Machine Translation based on Deep Learning Algorithm;2023 4th International Conference for Emerging Technology (INCET);2023-05-26

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