Abstract
In order to solve the problems of time-consuming and frequent mistranslations in traditional translation methods, this study designed an English-Chinese machine translation method based on transfer learning. On the basis of analyzing the basic principles and specific strategies of English-Chinese machine translation, the traditional neural machine translation methods are analyzed, and then the translation process is optimized by transfer learning. On the basis of preprocessing English-Chinese translation text data, features of English-Chinese translation text are extracted, features of English-Chinese translation text are rapidly classified by feature transfer learning, and machine models of English-Chinese translation are constructed based on the classification results. The objective of feature transfer learning is to reuse the past knowledge obtained in the form of dataset to be utilized for another target data. The findings of the experiment show the effectiveness of the proposed method in achieving the design expectation. The benefit of the proposed method includes a short translation time and fewer mistranslations.
Highlights
Research papers on the new dynamics of machine translation were published by IBM researchers in the late 1980s and early 1990s. ese papers gave a detailed explanation of the traditional machine translation method based on dictionaries and conversion rules and the instance machine translation method based on a parallel corpus. e role of these research papers is remarkable in promoting the consolidation and expansion of machine translation theory and the practice and formation of new methods and rules
After applying the multilevel template, words and phrases are divided into different levels, and the corresponding relationship between English and Chinese sentences is clearly reflected by combining the results of lexical annotation. is method only needs part of the corpus and a bilingual dictionary to complete EnglishChinese translation, which reduces the complexity of English-Chinese translation and improves the quality of translation
An English-Chinese machine translation method based on transfer learning is designed to fix the problem of time-consuming and frequent mistranslations. e neural machine translation methods are analyzed, and the translation process is optimized by transfer learning
Summary
As one of the fields with the longest history of computer technology application, machine translation has attracted the attention of linguists, philosophers, psychologists, scientists, engineers, and other scholars from different fields since its birth because it involves multiple disciplines [1]. The SEQ2SEQ model, which is the most useful and efficient attention-based neural machine translation model in the field of machine translation, does not consider the grammatical transformation between different languages This method suggests an optimized English-Chinese translation model, applying different text preprocessing and embedding layer parameter initialization methods and enhancing/improving SEQ2SEQ model structure by adding a translation layer for syntactic changes between encoder and decoder. In practical application, it is found that the above traditional methods have different degrees of the time-consuming translation process and many mistranslations To solve this problem, this study designs a transfer learning-based English-Chinese machine translation method
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