Data Correction Method of English Translation Error in Interactive Teaching Based on Genetic Algorithm

Due to the fact that English translation teaching often focuses on grammar rules and vocabulary memorization, it is difficult for people to handle a large number of complex contextual and semantic relationships in real time, and there are also significant errors in the actual translation process, resulting in data that is difficult to correct. Therefore, a data correction method for English translation errors in interactive teaching based on genetic algorithm is proposed. Introduce genetic algorithm to construct a similarity evaluation model and calculate English translation similarity. On this basis, weight the classified English translation samples; Optimize the genetic algorithm analysis framework for English translation, reduce data overlap, and improve the accuracy of part of speech recognition. The improved framework can more effectively handle part of speech recognition problems in English translation and improve translation quality. Combining multidimensional word vectors with the connection weight matrix of each layer, an encoder and decoder model is established, and an attention mechanism is added to the decoder model to enable the model to better understand semantic features and effectively identify and correct errors in English translation. Build a translation error correction model, use the translation error correction model to analyze semantic features, and achieve effective identification and correction of English translation errors. The experimental results show that this method performs well in correcting English sentence translation errors, with significantly better evaluation values for various sentence lengths than other methods, and can significantly improve the accuracy of translation.