Three-Way Decisions Based RNN Models for Sentiment Classification

Ma, Yan; Yu, Jingying; Ji, Bojing; Chen, Jie; Zhao, Shu; Chen, Jiajun

doi:10.1007/978-3-030-87334-9_21

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…With the development of computer technology and the maturity of artificial intelligence technology, machine translation is gradually replacing human translation and occupying a larger proportion in the translation field. At present, there are four main types of machine translation [1][2][3]. Among them, neural network-based machine translation models can alleviate the problem of feature design of high-dimensional data and improve the expressiveness of the model by building neural network classifiers when dealing with high-dimensional complex data, which has become the most popular and effective language translation model nowadays [4,5].…”

Section: Introductionmentioning

confidence: 99%

LSTM-Based Attentional Embedding for English Machine Translation

Jian

Xiang

2022

Scientific Programming

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In order to reduce the workload of manual grading and improve the efficiency of grading, a computerized intelligent grading system for English translation based on natural language processing is designed. An attention-embedded LSTM English machine translation model is proposed. Firstly, according to the characteristics of the standard LSTM network model that uses fixed dimensional vectors to represent words in the encoding stage, an English machine translation model based on LSTM attention embedding is established; the structure level of the English translation scoring system is constructed. A linguistic model of the English translation scoring system is established, and the probability distribution of a particular sentence sequence or word sequence of the translated text is statistically calculated using the model. The results show that the English machine translation model based on LSTM attention embedding proposed in this study can enhance the representation of the source language contextual information and improve the performance of the English machine translation model and the quality of the translation compared with the English machine translation models constructed by existing neural network structures, such as standard LSTM models, RNN models, and GRU-Attention translation models.

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