Neural machine translation incorporating dependencies

Abstract

The invention relates to a neural machine translation method incorporating dependency relation, which is designed for obtaining more accurate neural translation model. The invention integrates the dependency relation neural machine translation method, analyzes the dependency tree of the source sentence, and determines the relevance information between the source sentence words and the words. Basedon the dependency relation information, the dependency relevance loss Delta dep is determined, and the overall loss of the sentence pair network is obtained. The invention adds a self-attention mechanism at the source end, and integrates the self-attention mechanism into dependency guidance.

Description

Technical field

[0001] The invention belongs to the technical field of machine learning, and specifically relates to a neural machine translation method incorporating dependency relationships. Background technique

[0002] Machine translation refers to the technology of automatically converting one language (Source Language) into another language (Target Language) with the help of a computer. [Bahdanau et al., 2015] proposed to introduce the attention mechanism into neural machine translation, so that the effect of Neural Machine Translation (NMT) is gradually improved and gradually replaced Statistic Machine Translation (SMT). In 2017 [Vaswaniet al., 2017] proposed the Transformer model. The model fully uses the attention mechanism. The integration of multi-layer and residual networks has greatly improved the performance of neural machine translation. Researchers have improved the performance of the translation system based on the two models. , Large Internet companies are grad...

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