Construction method of semi-supervised neural machine translation model based on word-to-word translation

Abstract

The invention relates to a construction method of a semi-supervised neural machine translation model based on word-to-word translation, and belongs to the technical field of natural language processing. The method comprises the steps that firstly, monolingual corpora of a source language and a target language and parallel corpora of the source language and the target language are acquired; training a cross-language language model by using the monolingual corpus; initializing an encoder-decoder of the translation model by using the trained language model; acquiring bilingual dictionaries of thetwo languages; constructing a dictionary prefix tree in the translation model according to the bilingual dictionary; training an auto-encoder in the translation model; training a translation model byusing the parallel corpora of the source language and the target language; training the translation model and training the translation model at the same time; and training the translation model by using the parallel corpora of the source language and the target language through the auto-encoder, and fusing the translation model to obtain a final translation model. The method is simple and effective, the model can be translated normally, and the translation performance of the model is greatly improved.

Description

technical field [0001] The invention relates to a construction method of a semi-supervised neural machine translation model based on word-to-word translation, and belongs to the technical field of natural language processing. Background technique [0002] In the field of natural language processing, machine translation is a master of natural language processing and one of the most practical research subfields. Since supervised neural machine translation requires a large amount of parallel corpus, it is not very effective for language pairs that are difficult to obtain a large amount of parallel corpus, so unsupervised neural machine translation has been developed. In the Chinese-English language experiment of unsupervised neural machine translation, we found that the unsupervised neural machine translation model could not work properly due to the huge gap between Chinese and English languages. Therefore, two simple methods are proposed to improve it, so that the model can w...

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Problems solved by technology

[0003] The present invention provides a method for constructing a semi-supervised neural machine translation model based on word-to-word translation, which is used to construct a semi-supervised neural machine translation model, and solves the problem that the unsupervised translation model cannot translate normally between two languages ​​with a huge gap question

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