Method for translating German and mandarin Chinese with RBH (Random Black Hole) neural network model

A neural network model and neural network technology, applied in the application field of RBH neural network model in artificial intelligence, can solve the problem of high salary level of simultaneous interpretation, translation accuracy easily affected by personal physical factors, labor intensity of simultaneous interpreters major issues

Inactive Publication Date: 2018-10-09
湖南本来文化发展有限公司
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AI Technical Summary

Problems solved by technology

[0002] With the acceleration of the internationalization process, the demand for simultaneous interpretation is increasing. However, the existing simultaneous interpretation is done by people. Professional simultaneous interpretation personnel are labor-intensive, and the translation accuracy is easily affected by personal physical factors. In an international conference, if the conference lasts for a long time, the translator’s physical strength and energy will continue to be exhausted, and the accuracy of the translation will decrease due to fatigue; when individuals travel abroad, due to the high salary level of professional simultaneous interpretation, generally Ordinary people find it difficult to accept traveling with translators

Method used

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  • Method for translating German and mandarin Chinese with RBH (Random Black Hole) neural network model
  • Method for translating German and mandarin Chinese with RBH (Random Black Hole) neural network model
  • Method for translating German and mandarin Chinese with RBH (Random Black Hole) neural network model

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Embodiment 1

[0013] Example 1: In a long international conference that took up to 5 hours, the speakers spoke at a relatively fast speed. At this time, the RBH neural network model sent by the Chinese side used the machine to perform simultaneous translation of Mandarin and German. The machine was tireless and could always maintain the translation accuracy at a stable high level. Ability to work better than human translators.

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Abstract

The invention discloses a method for translating German and mandarin Chinese with an RBH (Random Black Hole) neural network model. In the method, six modules of (1) the RBH neural network model, (2) an audio large database of mandarin Chinese, (3) an audio large database of German, (4) a Chinese grammar database, (5) a German grammar database and (6) an audio acquisition and output device are included. Through the abovementioned modules, an RBH neural network translation model is enabled to replace professional senior translators so as to rapidly and efficiently provide simultaneous interpretations between mandarin Chinese and German for users at a low price.

Description

technical field [0001] The invention relates to the application field of the RBH neural network model in artificial intelligence, in particular to a method for translating German and Mandarin with the RBH neural network model. Background technique [0002] With the acceleration of the internationalization process, the demand for simultaneous interpretation is increasing. However, the existing simultaneous interpretation is done by people. Professional simultaneous interpretation personnel are labor-intensive, and the translation accuracy is easily affected by personal physical factors. In an international conference, if the conference lasts for a long time, the translator’s physical strength and energy will continue to be exhausted, and the accuracy of the translation will decrease due to fatigue; when individuals travel abroad, due to the high salary level of professional simultaneous interpretation, generally It is difficult for ordinary people to accept traveling with tra...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/28G06N3/08
CPCG06F40/42G06F40/56G06N3/08
Inventor 邱念
Owner 湖南本来文化发展有限公司
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