Neural machine translation method based on sample guidance

A machine translation and sample technology, applied in the neural field, can solve problems such as inapplicability of NMT, inability to fully use translation memory, incomplete solution, etc., to achieve the effect of avoiding interference

Pending Publication Date: 2019-07-23
SUZHOU UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0008] The method of combining statistical machine translation and translation memory involves intervention during translation, and NMT is a sequence-to-sequence structure. At present, intervention in NMT is still an unsolved problem, so the method in statistical machine translation is not applicable to NMT
[0009] The previous work combining NMT and translation memory can only match similar sentences when retrieving the translation memory, and then use the whole or part of the matched content to guide the translation, but if the whole information is used, because other parts of the sentence are different from The source sentence does not match, which inevitably introduces noise, and if only part of the matching content is used, then the information in the translation memory cannot be fully used

Method used

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  • Neural machine translation method based on sample guidance
  • Neural machine translation method based on sample guidance
  • Neural machine translation method based on sample guidance

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the examples given are not intended to limit the present invention.

[0037] Background: NMT model based on attention mechanism (attention)

[0038] In the neural machine translation system, the encoder-decoder framework is generally used to achieve translation. For each word in the training corpus, a word vector is initialized 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, for the word "we", its word vector may be .

[0039] Transformer is a model proposed by Google in 2017, the structure is as...

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Abstract

The invention discloses a neural machine translation method based on sample guidance. The invention relates to a neural machine translation method based on sample guidance, which comprises the following steps of assisting the translation of a source statement through the translation of a sentence similar to the source statement, finding the sample statement similar to the source statement in a sample database, and marking the sample statement as (x, xm), wherein x represents the source statement. The method for guiding translation of the sample by introducing the sample into the neural machinetranslation model has the advantages that the useless noise information in a sample statement is masked off through a noise-masked encoder model, and the interference of the useless information on source statement translation can be effectively avoided; and 2, through the auxiliary decoder model, which information in the model sample statement can be used can be displayed and guided, so that theinformation in the target end sample statement can be fully used.

Description

technical field [0001] The invention relates to the neural field, in particular to an example-based neural machine translation method. 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 (NMT) based on deep learning has attracted more and more attention. In the field of NMT, one of the most commonly used translation models is an attention-based encoder-decoder model. The main idea is to encode the 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 translations (collectively referred to as 'target sentences' hereinafter). [0003] In some special application scenarios, before translating the source sentence, a sentence similar to the source sente...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/28G06F17/22G06F16/33
CPCG06F16/334G06F40/194G06F40/58
Inventor 熊德意曹骞
Owner SUZHOU UNIV
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