Neutral machine translation method with copy mechanism

A machine translation and mechanism technology, applied in the field of neural machine translation, can solve the problems of inconsistent generated translations and low readability of translated texts

Active Publication Date: 2018-06-08
SUZHOU UNIV
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Problems solved by technology

This leads to a serious problem in the translation of NMT: when the source sentence to be translated contains words that are not in the vocabulary, UNK will be generated in the translation, resulting in low readability of th...

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  • Neutral machine translation method with copy mechanism
  • Neutral machine translation method with copy mechanism
  • Neutral machine translation method with copy mechanism

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Embodiment Construction

[0026] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0027] First, let me introduce the application basis of this application: the NMT model based on attention mechanism.

[0028] In the neural machine translation system, the encoder-decoder framework is generally used to achieve translation. For each word in the training corpus, we initialize a word vector for it, and the word vectors of all words constitute a word vector dictionary. A word vector is generally a multi-dimensional vector. Each dimension in the vector is a real number. The size of the dimension is generally determined according to the results of the experiment process. For example, ...

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Abstract

The invention relates to a neutral machine translation method with a copy mechanism. The method is applied to an NMT model based on an attention mechanism and adopting an encoder-decoder framework. The method includes the steps that source phrases needing to be translated specifically and corresponding target phrases are marked in a source language and a target language in original training sentences; the source phrases, needing to be translated specifically, in the source language in the original training sentences are replaced with the corresponding target phrases in the target language in the original training sentences; the processed original training sentences are subjected to training of an NMT system; phrases needing to be translated specifically are marked in the to-be-translated source sentences. By means of the neutral machine translation method with the copy mechanism, specific phrases such as person names, place names, mechanism names and brand names can be well translated,the method can be compatible with any corpus processing technology, the translation effect is further improved, the structure of the NMT system does not need to be changed, and the method can be conveniently applied to any NMT system.

Description

technical field [0001] The neural machine translation involved in the present invention, in particular, relates to a neural machine translation method with a replication mechanism. Background technique [0002] With the improvement of computer computing power and the application of big data, deep learning has been further applied. Neural Machine Translation based on deep learning has attracted more and more attention. In the NMT field, one of the most commonly used translation models is the encoder-decoder model with an attention-based mechanism. The main idea is to encode the source sentence to be translated (collectively referred to as 'source sentence' hereinafter) into a vector representation through an encoder, and then use a decoder to decode the vector representation of the source sentence, and translate it into Its corresponding translation (hereinafter collectively referred to as 'target sentence'). In fact, this encoder-decoder framework is the core idea of ​​de...

Claims

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Application Information

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IPC IPC(8): G06F17/28
CPCG06F40/58
Inventor 熊德意邝少辉
Owner SUZHOU UNIV
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