The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

Abstract

With the rapid development of big data and deep learning, breakthroughs have been made in phonetic and textual research, the two fundamental attributes of language. Language is an essential medium of information exchange in teaching activity. The aim is to promote the transformation of the training mode and content of translation major and the application of the translation service industry in various fields. Based on previous research, the SCN-LSTM (Skip Convolutional Network and Long Short Term Memory) translation model of deep learning neural network is constructed by learning and training the real dataset and the public PTB (Penn Treebank Dataset). The feasibility of the model’s performance, translation quality, and adaptability in practical teaching is analyzed to provide a theoretical basis for the research and application of the SCN-LSTM translation model in English teaching. The results show that the capability of the neural network for translation teaching is nearly one times higher than that of the traditional N-tuple translation model, and the fusion model performs much better than the single model, translation quality, and teaching effect. To be specific, the accuracy of the SCN-LSTM translation model based on deep learning neural network is 95.21%, the degree of translation confusion is reduced by 39.21% compared with that of the LSTM (Long Short Term Memory) model, and the adaptability is 0.4 times that of the N-tuple model. With the highest level of satisfaction in practical teaching evaluation, the SCN-LSTM translation model has achieved a favorable effect on the translation teaching of the English major. In summary, the performance and quality of the translation model are improved significantly by learning the language characteristics in translations by teachers and students, providing ideas for applying machine translation in professional translation teaching.

[Retracted] Human‐Computer Interaction Environment Monitoring and Collaborative Translation Mode Exploration Using Artificial Intelligence Technology

“…Human-computer interaction (HCI) is the process of merging computers and people in order to fully use the logical reasoning capabilities of computers and the general qualitative cognitive capabilities of humans. The left to right interaction framework is used in conventional interactive machine translation [19]. In the human-computer interaction framework, given a sentence in the source language to be translated, the user can perform translation completion or error correction from left to right.…”

Application of Production-Oriented Approach and Deep Learning Model in English Translation Teaching Model

“…It is an important task of English teaching in the era to cultivate advanced and complex English translation talents with good comprehensive application ability [1]. In educational practice, the training methods are mostly focused on "teacher-centered" and "student-centered."…”

Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques

“…Because of its unique design structure, it is suitable for handling and predicting important events with long intervals and delays in time series. Currently, application areas include text generation [ 28 ], speech recognition [ 29 ], machine translation [ 30 ], and infectious disease prediction [ 31 ]. LSTM is a special kind of recurrent neural network (RNN) [ 32 ], which combines short-term and long-term memory through gate control, overcomes the gradient disappearance or gradient explosion of traditional RNN models, and is better at dealing with the problem of multiple variables.…”