Abstract
With the development of recent economic globalization, international exchanges and cooperation are increasingly frequent and in-depth. In this process, there is a serious obstacle, that is, language differences. Therefore, the development of high-quality and practical machine translation system is of great significance. Japan has a close relationship with China, so it is necessary to acquire and process Japanese information. Japanese translation is the basis of Japanese information processing, which plays an important role in cross language information retrieval, machine translation, information extraction and other practical applications. In recent years, machine translation has made remarkable progress, but there is still much room for improvement in the quality of translation. Multimedia assisted instruction is an important application of computer technology. Therefore, this paper realizes the feature recognition and hardware structure of Japanese machine translation; for the efficient implementation of the model, the edge-driven model is combined. The proposed model is implemented through the edge devices and the performance of the model is validated through the testing. The recognition accuracy is much higher than the traditional models and the robustness is higher.
Highlights
With the rapid increase of information in today's world, the communication and cooperation between people who use different languages are more and more frequent and in-depth
In order to improve the quality of machine translation, automatic error detection and classification plays an important role in Machine translation (MT) output post-processing
There is still much room for theoretical research and system implementation of machine translation, and further research on machine translation is of great theoretical value and practical significance
Summary
With the rapid increase of information in today's world, the communication and cooperation between people who use different languages are more and more frequent and in-depth. Machine translation belongs to the subfield of computational linguistics, and the combination of artificial intelligence and natural language processing is its important research content. The traditional teaching method of translation course, which takes teachers as the center and book content as the main line, can not meet the needs of the new form. In order to improve the quality of machine translation, automatic error detection and classification plays an important role in MT output post-processing. Translation error judgment, classification and analysis is one of the important research contents of SMT technology development and application. Most translation error detection methods use system features such as word posterior probability (WPP) to estimate translation confidence, and use shallow syntactic or semantic knowledge as assistance to reduce classification error rate and improve error prediction performance. At present, lexical features such as posteriori probability, word and part of speech still play a major role [9–12]
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