Translation method and device, training method and device for translation model

Abstract

The invention provides a translation method and device, a translation model training method and a translation device, wherein, the translation method comprises the following steps of: obtaining a sentence to be translated; inputting the sentence to be translated into a translation model to obtain a translation sentence corresponding to the sentence to be translated, wherein the translation model is trained in the following ways: the first sentence in the target corpus and the translated second sentence are used as training samples and training features respectively; inputting the training sample to the coding layer of the translation model to obtain at least one coding vector; inputting at least one coding vector to a weight layer of the translation model to obtain a weight vector; inputting the training feature and the weight vector to a decoding layer of the translation model, obtaining a decoding vector, and obtaining an error of the decoding vector according to the decoding vector;adjusting the weight layer according to an error of the decoding vector, and continuing to train the translation model until a training stop condition is reached.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a translation method and device, a translation model training method and device, computing equipment, and a computer-readable storage medium. Background technique [0002] With the improvement of computer computing power, the application of neural networks has become more and more extensive, such as building translation models to realize the conversion from source language to target language. Generally, the architecture of a translation model includes: an encoder layer (encoder)-a decoder layer (decoder). The encoding layer realizes encoding the source sentence to be translated to generate a vector, and the decoding layer realizes decoding the vector of the source sentence to generate the corresponding target sentence. [0003] Most traditional translation models use Convolutional Neural Networks (CNN) models or Recurrent Neural Networks (RNN) models as the model...

AI Technical Summary

Problems solved by technology

[0008] In the Google translation model, the output vector of the last encoding stack layer is used as the final output vector of the encoding layer, where the output vector of each encoding stack layer will be the final The output vector has an influence, but the influence weight of each encoding stack layer is fixed, which will adversely affect the final translation effect

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