Design of English Translation Mobile Information System Based on Recurrent Neural Network

To solve the problem of translating lines of difference in length into English, this article presents a model of neural network recovery (RNN) English translator-based models of end-to-end encoder-decoder. This method promotes machine autonomous learning of features and transforms corpus data into w...

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Bibliographic Details
Published in:Mobile information systems 2022-08, Vol.2022, p.1-7
Main Author: Gao, Yue
Format: Article
Language:English
Subjects:
Brain research
Coders
Encoders-Decoders
English language
Interdisciplinary subjects
Interpreters
Machine translation
Native languages
Natural language processing
Neural networks
Recurrent neural networks
Research methodology
Semantics
Training
Translating
Translation
Translations
Translators
Citations: Items that this one cites
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Description
Summary:To solve the problem of translating lines of difference in length into English, this article presents a model of neural network recovery (RNN) English translator-based models of end-to-end encoder-decoder. This method promotes machine autonomous learning of features and transforms corpus data into word vectors by constructing end-to-end. By mapping the source language and target language directly through the recurrent neural network and selecting semantic error to construct objective function during training, the influence of each part in semantic can be well balanced, and the alignment information is fully considered, which provides powerful guidance for deep recurrent neural network training. The results of the neural network test define the standard BLEU score by 1.51–11.86. Our test scores and BLEU scores at all levels show that data in equivalence play an important role in modeling. Summary. the English translation model based on the neural repetitive fusion is efficient and stable.
ISSN:1574-017X
1875-905X
DOI:10.1155/2022/8053285