Multisensor Feature Fusion-Based Model for Business English Translation

Abstract

With the wide use of computers, machine translation has been gradually applied in many fields from natural language processing, such as industry, education, and so on. Due to the increasing demand for multilanguage translation, it is an urgent problem to effectively improve the quality of text translation. Driven by the upsurge of artificial intelligence, neural network technology is increasingly integrated into the field of machine translation, which gradually expands the traditional machine translation method into neural machine translation method. With the continuous improvement of deep learning technology, machine translation has gradually integrated these methods and strategies and achieved good results in multiple tasks, but there are still some shortcomings. The most prominent problem is that, since word vector is the basis for the model to obtain semantic and grammatical information, the existing methods cannot obtain semantic and grammatical feature information, which greatly reduces the accuracy of English translation. Based on this, this paper proposed a method of splicing word vector with character- level and word-level encoding vector. The characterization of fusion of more word vector can effectively solve the word does not appear in the table, the word with some low frequency, can express meaning more complete information, performance directly affects the whole translation model, the results can be seen through the experiment, we put forward the characteristics of the fusion method and strategy, can effectively enhance the overall translation performance of the model.