Depthwise separable convolution-based Chinese-Vietnamese neural machine translation method

Abstract

The invention relates to a depthwise separable convolution-based Chinese-Vietnamese neural machine translation method, and belongs to the technical field of resource scarcity type language neural machine translation. According to language characteristics of Vietnamese, Vietnamese is segmented into four different translation granularity sequences of words, syllables, characters and sub-words; secondly, a neural machine translation model is improved by utilizing depthwise separable convolution; a depthwise separable convolutional neural network is added; convolution operation is carried out on different granularity sequences input by the model and more feature data are extracted. By adding the depthwise separable convolutional neural network prior to an encoder embedding layer of the neuralmachine translation model, The depthwise separable convolution-based Chinese-Vietnamese neural machine translation method is constructed in the field of resource scarcity type language machine translation, so that the problem of sparse data of the resource scarcity type language neural machine translation is effectively relieved, and the translation performance is improved.

Description

technical field [0001] The invention relates to a Chinese-Vietnamese neural machine translation method based on depthwise separable convolution, and belongs to the technical field of resource-scarce language neural machine translation. Background technique [0002] In recent years, Neural Machine Translation (NMT) has become an important direction of machine translation research. With the increasingly frequent development of Sino-Vietnamese relations, Chinese-Vietnamese neural machine translation is of great significance and value to Sino-Vietnamese exchanges. Vietnamese is a resource-scarce language. It is characterized by the lack of bilingual alignment corpus and difficulty in corpus collection, which makes the problem of data sparseness very serious, which greatly affects the translation effect of the model. [0003] In neural machine translation, in order to alleviate the problem of data sparseness, smaller input / output granularity is generally used to solve the proble...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a Chinese-Vietnamese neural machine translation method based on depthwise separable convolution to solve the problem of data sparseness in resource-scarce language neural machine translation, and the method significantly improves the performance of Chinese-Vietnamese neural machine translation

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