Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Abstract

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on the BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of the BP neural network algorithm, taking English online translation as the research object, Google’s translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301, respectively, which is much higher than Google translation. A model of machine translation evaluation based on the neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on the neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation.

The Impact of Artificial Intelligence on Language Translation: A Review

“…References [21], [22], [50], [35], [78], [82], [83], [84], [85], and [91] provide a detailed analysis of machine translation pertaining to particular languages or dialects, including Arabic, Urdu, Sana'ani, and Moroccan Arabic. Furthermore, the evaluation and increase in machine translation quality were examined in [3], [21], [29], [32], [49], [58], [86], [75], [78], [80], [85], [87], and [92]. References [7], [6], [63], [87], [69], [82], [88], [89], and [90] pertain to the wider scope of artificial intelligence in translation, including aspects such as education and the influence on professionals.…”

“…The use of fuzzy logic and feature extraction algorithms in translation and language analysis was discussed in [60], [61], [62], [63], [67], [68], [69], [77], [79], [82], and [90]. Regarding Human Evaluation and Cognitive factors, it has been observed that references [71], [76], [78], [80], [81], [88], and [92] analyzed the significance of human evaluation and cognitive factors in assessing the quality of translation. Finally, a detailed exploration of ethics in translation and its associated considerations is encapsulated within references [93] through [98], presenting a comprehensive discourse on this critical subject.…”

“…In this scholarly article, authored by Liu [80], a new framework is presented for assessing the efficacy of machine-generated translations. This model is constructed based on deep learning methodologies with the objective of delivering accurate evaluations of the quality of machinegenerated translations.…”

Neural machine translation in AVT teaching in China: An in-depth analysis from the readability perspective

“…However, their human error analysis also indicates that there is still room for improvement in NMT output. Similarly, Han and Meng (2022) evaluated the quality of Chinese-English online translation using the BP neural network algorithm. They found that Baidu translation and iFLYTEK translation have a much higher error rate than Google Translate.…”

Retracted: Machine English Translation Evaluation System Based on BP Neural Network Algorithm

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”