Research on Transfer Learning-Based English-Chinese Machine Translation

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.

Research on adaptive model of English translation based on data fusion

“…The main principle is to quickly scan the whole scene of its visual interval through the human visual system, select the key target interval from it through the signal processing mechanism of the brain, and put more attention resources into the interval. In the process of English language translation, many key value pairs can be searched through the attention mechanism, and the coincidence degree of such key value pairs can be obtained [13][14][15]. The coincidence degree is proportional to the amount of attention resources allocated.…”