Research on NLP Based Chinese English Translation Text Quality Rating Model

The current Chinese and English translation scoring system has defects such as long working times and significant differences between system scoring data and human scoring data. To reduce the difficulty of human scoring and improve scoring effectiveness, an artificial intelligence scoring management platform for English translation based on natural language information processing was developed. A hierarchical structure of the Chinese-English translation scoring platform was established with the functions of translator material collection, message feature acquisition, language analysis model construction, and data transmission and evaluation. Then, a sentence analysis model for the Chinese-English translation scoring management platform is created, and the performance is statistically analyzed for the probability distribution of certain sentences or vocabulary orders in the translation for the evaluation of the intelligence of the Chinese-English translation network platform. The experimental results show that the minimum error between the scoring of the proposed platform and the traditional manual scoring is only 0.1 points and the accuracy of the evaluation results is better than that of the current scoring platform. The running time for scoring is shorter than that of the current scoring platform with more stable and better quality.