Text-level neural machine translation method based on context memory network

Abstract

The invention discloses a text-level neural machine translation method based on a context memory network. The method comprises the following steps: constructing a Transformer model of the context memory network; constructing a parallel corpus to obtain word embedding input of the model; at an encoder end, enabling source language input to be subjected to encoding representation in combination witha context memory module, and updating the current encoding representation into the context memory module; at a decoder end, processing the target language in combination with the source language coding representation to obtain vector representations with consistent lengths; performing softmax normalization operation on an output result of the decoder to obtain predicted distribution, and completing a training process of the model; and carrying out chapter-level machine translation by utilizing the trained model, sending the text into the model sentence by sentence for translation, and obtaining a translation result in an autoregression mode. The context memory module is added to dynamically maintain context memory information, related context information is introduced, and the problem that translation result contexts are inconsistent is solved.

Description

technical field [0001] The invention relates to a neural machine translation technology, in particular to a text-level neural machine translation method based on a context memory network. Background technique [0002] Machine Translation (MT for short) is an experimental discipline that uses computers to translate between natural languages. Using machine translation technology, a source language can be automatically converted into a target language. Machine translation, as a key technology to eliminate barriers in people's cross-language communication, has always been an important part of natural language processing research. Compared with human translation, machine translation is more efficient and lower cost, which is of great significance for promoting national unity and cultural exchanges. Machine translation technology can be summarized as two methods based on rationalism and methods based on empiricism. Since it was proposed in the 1940s, machine translation has expe...

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Problems solved by technology

[0008] Aiming at the shortcomings of the multi-encoder method in text-level neural machine translation, such as low computational efficiency and insufficient context information, the present invention proposes a text-level machine translation method based on context memory network, which can understand the source code according to human writing and speaking habits. Efficiently represent the context information before the sentence to be translated, and introduce it into the translation process

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