Mongolian and Chinese neural machine translation method based on transfer learning strategy

Abstract

The invention is proposed for solving the problem of low quality of translated texts and poor translation effect of a current Mongolian and Chinese machine. Because the Mongolian language belongs to low-resource language, a large quantity of Mongolian and Chinese parallel bilingual corpus can be collected difficultly. A transfer learning strategy is a method for solving the problems in the different but relevant fields by using existing knowledge. The method comprises the following steps: firstly, performing training by utilizing the large-scale English-Chinese parallel corpus based on a neural machine translation frame; secondly, transferring translation model parameters trained by the large-scale English-Chinese parallel corpus into the Mongolian and Chinese neural machine translation frame, and training a neural machine translation model by utilizing the existing English-Chinese parallel corpus; and finally, performing contrast and evaluation on texts translated by a neural machinebased on the transfer learning strategy and texts translated by a statistical machine in accordance with BLEU values and the fluency of the translated texts. Through operation of a control variable method, the transfer learning strategy is obtained, so that the translation performance of a Mongolian and Chinese machine is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of neural machine translation, and in particular relates to a Mongolian-Chinese neural machine translation method based on a migration learning strategy. Background technique [0002] Machine translation refers to the process of using a machine (computer) to automatically convert a natural language into another natural language with exactly the same meaning. In recent years, with the increasing frequency of international exchanges, machine translation, as an important means of breaking through language barriers, is playing an increasingly important role in people's production and life. As one of the data-driven methods of machine translation, neural machine translation is highly dependent on the scale and quality of parallel corpus data structures. Due to the large scale of neural network parameters, neural machine translation will significantly exceed the translation quality of statistical machine translat...

AI Technical Summary

Problems solved by technology

This solves the problem of insufficient Mongolian-Chinese parallel corpus and achieves the goal of improving the performance of Mongolian-Chinese machine translation

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