A Study on Error Feature Analysis and Error Correction in English Translation Through Machine Translatio

Abstract

English translation is the most frequently encountered problem in English learning, and fast, efficient and correct English translation has become the demand of many people. This paper studied the most frequently encountered English grammatical error problem in English translation by the Transformer grammatical error correction model in machine translation and explored whether machine translation could analyze the features of the errors that may occur in English translation and correct them. The results of the study showed that the precision of the Transformer model reached 93.64%, the recall rate reached 94.01%, the  value was 2.35, and the value of Bilingual Evaluation Understudy was 0.94, which were better than those of the other three models. The Transformer model also showed stronger error correction performance than Seq2seq, convolutional neural network, and recurrent neural network models in analyzing error correction instances of English translation. This paper proves that it is feasible and practical to identify and correct English translation errors by machine translation based on the Transformer model.