Training method and device of translation model, text processing method and device and storage medium

Abstract

The invention provides a training method of a translation model. The training method comprises the steps of obtaining a first training sample set; denoising the first training sample set to form a corresponding second training sample set; processing the first training sample set through a translation model to determine initial parameters of the translation model; responding to the initial parameters of the translation model, processing the second training sample set through the translation model, and determining updating parameters of the translation model; and iteratively updating encoder parameters and decoder parameters of the translation model through the first training sample set and the second training sample set according to the updating parameters of the translation model. The invention further provides a text processing method and device and a storage medium. According to the method, the generalization ability of the translation model can be stronger, the training precision and the training speed of the translation model are improved, and meanwhile, the gain of existing noise statements on model training can be effectively and fully utilized, so that the translation modelcan adapt to different use scenes.

Description

technical field [0001] The present invention relates to machine translation (MT, Machine Translation) technology, in particular to a translation model training method, text processing method, device and storage medium. Background technique [0002] With the development of machine translation, Neural Machine Translation (NMT, Neural Machine Translation) has been widely used as a new generation of translation technology. The neural network machine translation system is built based on the encoder-decoder framework. However, in the translation process of the neural network machine translation system, the decoder has multiple tasks, such as recording the current translated content and the content to be translated, and recording the translated content. Fluency related information, etc. Due to the complexity of the decoder task, it requires high-precision training samples that have been denoised. For small languages ​​or languages ​​that lack training samples, the noise interferen...

AI Technical Summary

Problems solved by technology

Due to the complexity of the decoder task, it requires high-precision training samples that have been denoised. For small languages ​​or languages ​​that lack training samples, the

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