Method for constructing Mongolian-Chinese parallel corpora by utilizing generative adversarial network to improve Mongolian-Chinese translation quality

Abstract

The invention discloses a method for constructing Mongolian-Chinese parallel corpora by utilizing a generative adversarial network to improve Mongolian-Chinese translation quality. The generative adversarial network comprises a generator and a discriminator. The generator uses a hybrid encoder to encode a source language sentence Mongolian into vector representation and converts the representationinto target language sentence Chinese by using a decoder based on a bidirectional Transformer in combination with a sparse attention mechanism. Therefore, Mongolian sentences and more Mongolian-Chinese parallel corpora which are closer to human translation are generated. In a discriminator, the difference between the Chinese sentence generated by the generator and human translation is judged; andadversarial training is performed on the generator and the discriminator until the discriminator considers that the Chinese sentence generated by the generator is very similar to the human translation, a high-quality Mongolian-Chinese machine translation system and a large number of Mongolian-Chinese parallel data sets are acquired and Mongolian-Chinese translation is performed by using the Mongolian-Chinese machine translation system. According to the method, the problems that Mongolian-Chinese parallel data sets are severely deficient and NMT cannot guarantee naturalness, sufficiency and accuracy of translation results are solved.

Description

technical field [0001] The invention belongs to the technical field of machine translation, and in particular relates to a method for improving Mongolian-Chinese translation quality by using a generative confrontation network to construct Mongolian-Chinese parallel corpus. Background technique [0002] Machine translation, which can use computers to automatically translate one language into another, is one of the most powerful means to solve language barriers. In recent years, many large search companies and service centers such as Google and Baidu have conducted large-scale research on machine translation, making important contributions to obtaining high-quality translations of machine translation, so the translation between major languages ​​is close to that of human beings. At the translation level, millions of people communicate across language barriers using online translation systems and mobile apps. In the wave of deep learning in recent years, machine translation ha...

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Problems solved by technology

[0006] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a method for improving the quality of Mongolian-Chinese translation by using a generative con

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