Research on Automatic Detection and Correction of English Translation Errors Based on Machine Learning

With the rapid development of machine translation technology, its application in various language processing tasks is becoming more and more extensive. However, errors in translation results are still unavoidable, which not only affects the readability and accuracy of the translation, but also limits the further promotion and application of machine translation systems. English, as a world language, occupies a pivotal position in world economic and social development. English translation has always been a key research topic in the field of linguistics. Automatic detection and correction of English translation errors has become one of the important research directions in the field of natural language processing. This paper proposes an efficient automated solution based on machine learning technology. It constructs a labeled dataset covering multiple error types, designs a detection framework that combines deep learning models and linguistic features, and discusses the application of sequence-to-sequence models in error correction. Solving the problem of language level between Chinese and English is behind the huge cultural differences that lead to communication problems. The proposed method shows good detection and correction performance in a variety of translation scenarios, providing strong support for further improving the quality of machine translation.