Error Correction Method for English Sentence Translation Based on Deep Learning

In order to improve the accuracy and fluency of English sentence translation, the study explores how to improve the correctness and fluency of translation based on the deep learning translation error correction method, while ensuring the quality of translation. On this basis, a multi-layer model is constructed to study the attention mechanism, deep convolutional neural network, and key technologies such as word vector generation to achieve correct identification and optimization of translations. The validation on WMT English German and OpenSubtitles datasets shows that the BLEU score is improved from 26.5 to 41.2, the error rate is reduced from 0.34 to 0.18, the grammatical errors are reduced to 12, and the average translation time is only 0.8 seconds. The test on different sentence types proved that this model has better recognition effect on medium and short sentences. The research provides strong theoretical support for translation optimization of complex semantic structures and expands the error correction methods in multilingual scenarios.