Machine Translation: A Survey of Active Systems

Bátori, István S.; Lenders, Winfried; Putschke, Wolfgang

doi:10.1515/9783110097924.11.629

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When a piece of English is input, the database will make a quick data comparison, complete the connection between Chinese and English, and output the Chinese. However, in this process, only data classi cation and extraction will be carried out, and the input English will not be further analyzed [8][9]. The literature proposed a neural network language model [10].…”

Section: Related Workmentioning

confidence: 99%

Model optimization of English intelligent translation based on Outlier detection and machine learning

Bian

Zhao

2023

Preprint

View full text Add to dashboard Cite

With the actual demand of international communication, intelligent translation of English has become a key direction of artificial intelligence development in the current English field. For the existing English intelligent translation, how to deal with the massive data effectively is always a big problem. Therefore, it is necessary to use the principle of machine learning to optimize the English intelligent translation model. The purpose of this paper is to optimize the existing English intelligent translation model through spectral clustering to remove outliers, so as to make it more suitable for the use of massive data. Moreover, this paper uses deep learning methods to improve on the basis of the PoseNet network structure and adds regularization to the convolutional layer, which reduces the problem of gradient disappearance and reduces the computational complexity. In addition, this paper uses adaptive weighting to remove invalid model assumptions. In the conceptual space of the similarity matrix, the interior point is farther from the origin than the outlier point. The algorithm in this paper can detect and eliminate the outliers in a paragraph of English. At the same time, the normal English content will be classified into data categories, and the final translation result will be obtained. Through the experimental test, it can be seen that the model proposed in this paper has good performance and can cope with massive data, so it has certain superiority.

show abstract

Section: Related Workmentioning

confidence: 99%