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Parallel Algorithm of Hierarchical Phrase Machine Translation Based on Distributed Network Memory

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  • Guanghua Qiu

    (Henan University, China)

Abstract

Machine translation has developed rapidly. But there are some problems in machine translation, such as good reading, unable to reflect the mood and context, and even some language machines can not recognize. In order to improve the quality of translation, this paper uses the SSCI method to improve the quality of translation. It is found that the translation quality of hierarchical phrases is significantly improved after using the parallel algorithm of machine translation, which is about 9% higher than before, and the problem of context free grammar is also solved. The research also found that the use of parallel algorithm can effectively reduce the network memory occupation, the original 10 character content, after using the parallel algorithm, only need to occupy 8 characters, the optimization reaches 20%. This means that the parallel algorithm of hierarchical phrase machine translation based on distributed network memory can play a very important role in machine translation.

Suggested Citation

  • Guanghua Qiu, 2022. "Parallel Algorithm of Hierarchical Phrase Machine Translation Based on Distributed Network Memory," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 15(1), pages 1-16, January.
    • Handle: RePEc:igg:jisscm:v:15:y:2022:i:1:p:1-16
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