Machine English Translation Evaluation System Based on BP Neural Network Algorithm.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Translation Evaluation

Background:

• Traditional machine translation systems face challenges in efficiency and quality.
• Existing evaluation methods may not fully capture nuances of translation accuracy.

Purpose of the Study:

• To propose an intelligent English translation evaluation system using the BP neural network algorithm.
• To compare the performance of different online machine translation services.

Main Methods:

• Implementation of a BP neural network algorithm for machine translation evaluation.
• Analysis of error frequencies for Google Translate, Baidu Translate, and iFLYTEK Translate.

Main Results:

• Google Translate exhibited the lowest error frequency (167) compared to Baidu Translate (266) and iFLYTEK Translate (301).
• The BP neural network-based system effectively addresses issues like information underutilization and large model parameters in machine translation.

Conclusions:

• The BP neural network algorithm offers a more intelligent and effective approach to machine translation evaluation.
• This method optimizes machine translation performance, improving overall quality and efficiency.