Abstract
With the development of deep learning methods, the machine translation system based on deep neural network has reached a very high accuracy, but for some daily Chinese phenomenon machine translation system is still not able to translate correctly. In this paper, we study a sentence that often appears in Chinese spoken language, that is, a simple state sentence composed of quantitative phrases, and improve the existing machine translation system. The external helper program constructed in this paper is compatible with the current mainstream network translation systems, greatly improving the translation effect of these translation systems on the concise state sentences composed of quantitative phrases.
Highlights
Sentence complemThe whole machine translation system consists of three parts: the first part finds the collocation relationship and collocation words between the two quantifier phrases and noun components of a concise state sentence by searching the collocation thesaurus, and extracts all the corresponding words of the collocation words
With the development of deep learning methods, the machine translation system based on deep neural network has reached a very high accuracy, but for some daily Chinese phenomenon machine translation system is still not able to translate correctly
We study a sentence that often appears in Chinese spoken language, that is, a simple state sentence composed of quantitative phrases, and improve the existing machine translation system
Summary
The whole machine translation system consists of three parts: the first part finds the collocation relationship and collocation words between the two quantifier phrases and noun components of a concise state sentence by searching the collocation thesaurus, and extracts all the corresponding words of the collocation words. The second part of the sentence adjustment, combined with the existing Chinese corpus, select the most appropriate collocation words and the sentence to be translated to form a clear meaning of the sentence to be translated; In the last part, the complete sentence to be translated is input into the neural network-based machine translation system built by Google (hereinafter referred to as GMT) to get a relatively correct translation result. In theory, this method can improve the accuracy of the translation results, compared with the direct use of Google translation, can improve the translation effect. The specific experimental results are shown in the following table:
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