Research on Business English Translation Framework Based on Speech Recognition and Wireless Communication

Wu, Leida; Wu, Lianguan

doi:10.1155/2021/5575541

Cited by 27 publications

(18 citation statements)

References 20 publications

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“…Mechanical translation has gradually replaced artificial language translation, but mechanical translation of English-Chinese scientific and technological literature is not ideal. The construction of the neural network language model increases the intraword structure modeling and merging layer to form a new neural network language model [ 27 , 28 ]. It provides the possibility to improve the effect of machine translation of scientific and technological literature in English and Chinese.…”

Section: Construction Of the Machine Translation Model Based On Deep ...mentioning

confidence: 99%

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Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Sun

2022

Computational Intelligence and Neuroscience

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With the advent of the information age, people can establish good communication through Internet technology. Mechanical translation has become a key means to solve people’s communication problems. However, there are still obstacles to communication between different languages. In order to solve this problem, this paper uses existing neural network technology to the English-Chinese bidirectional machine translation model in the field of marine science and technology. Based on deep learning technology, we collect Chinese and English abstracts and partial full texts of Chinese and English papers with marine science and technology as the key words and build a professional corpus in English and Chinese about marine science and technology. In the Chinese-English bidirectional translation model, the local weight sharing is introduced into the Chinese encoder and the English encoder, and the output of the Chinese encoder sublayer and the English encoder sublayer is fused as the output of the respective encoders, and the performance of the translation model is evaluated using the BLEU parameters. Through the training of the translation model, compared with the transformer model, the BLEU value of the model with local weight sharing and encoder sublayer fusion output is improved by 1.6 and 3.8 in the Chinese-English and English-Chinese translation directions, respectively. The PPL values in the Chinese-English and English-Chinese translation directions decreased by 18.72% and 14.62%, respectively. We demonstrate the effectiveness of the language translation model. Experiments show that the research on machine language adaptive technology based on deep learning can more smoothly realize the two-way translation of literature in the field of marine science and technology. Compared with traditional mechanical translation, this paper proposes a translation model based on the deep neural algorithm, which improves the effect of model training by constructing a Chinese-English corpus with the theme of marine science and technology.

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Section: Construction Of the Machine Translation Model Based On Deep ...mentioning

confidence: 99%

“…e construction of the neural network language model increases the intraword structure modeling and merging layer to form a new neural network language model [27,28]. It provides the possibility to improve the effect of machine translation of scientific and technological literature in English and Chinese.…”

Section: Principles Of a Mechanical Translation Modelmentioning

confidence: 99%

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Sun

2022

Computational Intelligence and Neuroscience

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“…An optimized translation model is also applied for translation error correction and intelligent checking to increase the accuracy and accuracy of translations. The results of the trial show that the system has a high correction rate and error rate, and the translating results have certain level of stability, which fully validates the system's validity and application value but has no wide application [4]. With the advancement of information technology and quality training, to enhance the comprehensive quality and subjective motivation of students, Wu has explored some new teaching methods and teaching methods.…”

Section: Introductionmentioning

confidence: 89%

Design and Implementation of Interactive English Translation System in Internet of Things Auxiliary Information Processing

Hou

Li-hong

2022

Wireless Communications and Mobile Computing

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Information technology has penetrated into all aspects of human life. Nowadays, with the rapid development of science and technology, information technology has gradually become the cornerstone of the development of other technologies. The Internet of Things is an important part of the new generation of information technology. Language is the medium of communication between people. Driven by economic globalization and the development of the Internet, information is growing rapidly, and there are more and more exchanges and exchanges between countries. The emergence of high-efficiency and high-economic machine translation solves these difficulties, and the interactive English translation system is the current research hotspot, which is intended to improve the output translation quality of the English translation system. The main work of this paper is to analyze the existing interactive machine translation technology, especially the interactive machine translation based on a phrase model, using the Internet of Things as a knowledge source. According to the characteristics of segment analysis and human-computer interaction mechanism, from the network, a wealth of information are available from open sources. In this paper, on the basis of the segment analysis, the human-machine cooperation translation strategy of human-machine cooperation with complementary human-machine advantages was discussed, and the system designed was verified. It is proved that the system has high performance in improving the accuracy and recall rate of machine English translation. Compared with the existing English translation system, the accuracy has improved by more than 20% in the case of fewer iterations, and in the case of 90 iterations, the accuracy can improve by 100%.

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“…To make full use of this new model, the following aspects need to be considered. First, the training data must be annotated with other factors, which would involve running automated tools on the corpus, since manually annotated corpora of the texts are few and expensive to produce [3]. Second, a word matching mechanism needs to be built for all sentences in parallel training, and a word-layer alignment is used for word alignment.…”

Section: Introductionmentioning

confidence: 99%

Research on Computer Intelligent Proofreading System for English Translation Based on Deep Learning

2022

Wireless Communications and Mobile Computing

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English, as a global language, is widely used in daily life, which leads to higher requirements for people’s language proficiency. Grammar, as an essential part of English, plays a significant role in language acquisition. Although the needs for English learning have become greater, the resources of English teaching are quite limited. Under this circumstance, it is imperative to apply information technology to alleviate the burden of lacking sufficient educational resources. The intelligent grammar correction system is in special needs. In this study therefore, the deep learning-based CNN and RNN bidirectional propagation model were utilized to construct a feedback filtering function, screen the suggested text, and select the high-quality suggested text for the retraining of the error correction model. The proofreading optimization algorithm and feedback filtering algorithm were used in practice to implement each functional module. The core function service module was designed with a cluster deployment scheme with high system performance and response speed to ensure system stability and availability. Experimental results demonstrate that the correct rate gradually increased with increasing the number of tests and finally stabilized at about 86%. The overall average correcting rate obtained from the present model was verified to be superior to that from the GRU model and the MGB model.

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Research on Business English Translation Framework Based on Speech Recognition and Wireless Communication

Cited by 27 publications

References 20 publications

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Design and Implementation of Interactive English Translation System in Internet of Things Auxiliary Information Processing

Research on Computer Intelligent Proofreading System for English Translation Based on Deep Learning

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