Design of Intelligent Recognition Model for English Translation Based on Deep Machine Learning
Lecture Notes on Data Engineering and Communications Technologies, ISSN: 2367-4520, Vol: 138, Page: 774-779
2022
- 3Citations
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Machine translation has developed vigorously these years. However, there are still many problems in machine translation that need to be solved urgently. The aim of the thesis is to study the designing of intelligent recognition model for English translation based on deep machine learning. The neural translation system of this article separates larger words and sentences into translation, and then corrects errors, reducing the problem of poor neural translation performance when recognizing sentence length in English translation. Experimental research shows that the use of the English translation intelligent recognition model of this thesis improves the efficiency of 85% in the convergence speed of the original translation training.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85132829795&origin=inward; http://dx.doi.org/10.1007/978-3-031-05484-6_100; https://link.springer.com/10.1007/978-3-031-05484-6_100; https://dx.doi.org/10.1007/978-3-031-05484-6_100; https://link.springer.com/chapter/10.1007/978-3-031-05484-6_100
Springer Science and Business Media LLC
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