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Decoding method based on deep neural network translation model

A deep neural network and translation model technology, applied in the field of natural language processing, can solve problems such as slow decoding and difficult training, and achieve the effect of improving performance, improving decoding speed, and solving high training complexity.

Active Publication Date: 2018-10-12
INST OF AUTOMATION CHINESE ACAD OF SCI +1
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Problems solved by technology

[0005] In order to solve the above problems in the prior art, that is, in order to solve the problems of difficult training and slow decoding in the deep neural network machine translation model, the application provides a decoding method based on the deep neural network translation model to solve the above problems

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  • Decoding method based on deep neural network translation model

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[0043] Preferred embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention, and are not intended to limit the protection scope of the present invention.

[0044] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0045] figure 1 A flow chart of an embodiment of a decoding method based on a deep neural network translation model of the present application is shown.

[0046] like figure 1 As shown, the decoding method based on the deep neural network translation model of the present application includes the following steps:

[0047] Step 1: Segment the sentence to...

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Abstract

The invention relates to the field of the language processing, and provides a decoding method based on a deep neural network translation model. The problems of high model training complexity, large training difficulty and slow decoding speed in a machine translation model are solved. The specific implementation way of the method comprises the following steps: performing word segmentation processing on the to-be-translated sentence to obtain source language vocabulary; step two, performing word alignment on the linguistic data in a preset translation model glossary by using an automatic alignment tool so as to obtain a target language word aligned to the source language vocabulary; and step three, determining a target-side dynamic glossary of the to-be-translated sentence based on the target language word obtained in the step two, and using the sentence decoded by using a column searching method as the output of the translation model according to the pre-constructed translation model, wherein the translation model is the deep neural network based on the threshold residual mechanism and the parallel attention mechanism. Through the decoding method disclosed by the invention, the model translation quality is improved, and the mode coding speed is improved.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to a decoding method based on a deep neural network translation model. Background technique [0002] Machine translation, also known as automatic translation, is the process of using a computer to convert a natural language (source language) into another natural language (target language) with the same semantics. Machine translation is the process of converting from a source language to a target language. The system framework of machine translation can be divided into two categories: rule-based machine translation (RBMT) and corpus-based machine translation (CBMT). Among them, CBMT can be divided into instance-based machine translation (EBMT), statistical-based machine translation (SMT), and neural network machine translation (NMT) constructed using deep learning models that have become popular in recent years. [0003] Among them, the machine translation metho...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/28G06F17/27G06N3/04
CPCG06F40/289G06F40/58G06N3/045
Inventor 张家俊周龙马宏远杜翠兰张翠赵晓航宗成庆
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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