Research on English Translation Quality Assessment System Based on Machine Learning and Neural Network

This study proposes an English translation quality assessment system based on machine learning and neural network, aiming to quantitatively analyze translation results through automated methods and thus improve translation quality. The core of the system is to combine multiple machine learning algorithms and deep neural networks to evaluate the quality of translated texts. First, a translation quality assessment model is designed, and neural networks are used to conduct in-depth analysis of the grammatical structure, vocabulary selection, semantic matching, etc. of the translated text. Secondly, the evaluation criteria are optimized based on machine learning algorithms to ensure that the system can more accurately reflect the accuracy and fluency of the translation. The training process of the model uses a large-scale translation data set and simulates actual translation tasks to ensure the effectiveness of the evaluation system in various situations. The simulation results show that the system can effectively identify errors and deficiencies in translation, and its evaluation accuracy is significantly higher than that of traditional evaluation methods. Specific data show that the translation evaluation accuracy of the system has increased by 18%, and the translation error detection rate has increased by 22%.