Abstract
In recent years, with the continuous innovation and development of communication technology, different countries have become more closely connected, especially under the system of trade globalization, communication between different countries is more frequent, among which English is the One of the international common languages, the use of English language as an international communication reflects the importance of English translation (ET). This paper focuses on the research on translation systems that can only recognize English. ML (machine learning) algorithms can make feasible and effective results for large data sources in a relatively short period of time. Therefore, in the translation process, it is necessary to call internal and external resources, and use various translation strategies to understand the original text and output the translation. The ability to verify data plays an important role in this understanding and output process. This paper can greatly improve the efficiency and accuracy of ET through ML algorithms. After extracting a large number of translation rules, it is necessary to establish a probability model for the translation rules. The general methods include counting-based maximum likelihood estimation, EM method, discriminant training and other methods. The final results of the research show that the ML algorithm scores 91.7, 93.6, and 94.5 in terms of recognition speed, recognition accuracy, and update ability, respectively. Compared with the GLA algorithm, the ML algorithm has obvious advantages. The accuracy, and the ability to speed up the update are better.
Published Version
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