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Machine Learning Theory and Practice, 2021, 2(1); doi: 10.38007/ML.2021.020101.

AI machine Translation + Post Editing in English Chinese Translation under the Background of Big Data


Yun Li

Corresponding Author:
Yun Li

Nanchang Institute of Technology, Jiangxi 330099, China


In today's era of big data, due to the rapid development of software technology, new computing methods are constantly emerging, and computers are constantly improving their performance to meet everyone's needs. At the same time, the progress of machine translation technology has been recognized by the industry and customers. Important aid and efficient means. Machine translation is more and more widely used in today's translation activities, and its role and influence can not be underestimated. Some experts even predict that it will replace manual translation in the future. This paper aims to analyze and study the components of AI MT+Post editor in English-Chinese translation. This paper analyzes the results of intelligent translation and concludes that machine translation has the characteristics of randomness and time-varying. By applying graph theory and ID3 algorithm, it is established that AI MT+ post-editing is used in EC translation, and corresponding technologies are applied to prune classification decision trees. The classification rules are generated, and the classification model construction in EC translation of the above-mentioned post-editing is completed. The experimental results show that the translation + post-editing algorithm designed after the error correction of the GPC algorithm is applied, and the response time in the English-Chinese translation system is less, the overshoot is small, and the control effect is improved by 20% compared with the GPC before error correction.


Big Data, Artificial Intelligence, Machine Translation, Classification Decision Tree, Post Editing

Cite This Paper

Yun Li. AI machine Translation + Post Editing in English Chinese Translation under the Background of Big Data. Machine Learning Theory and Practice (2021), Vol. 2, Issue 1: 1-8. https://doi.org/10.38007/ML.2021.020101.


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