Research on grammatical error correction algorithm in English translation via deep learning

Abstract

Abstract This study provides a concise overview of a grammatical error correction algorithm that is based on an encoder-decoder machine translation structure. Additionally, it incorporates the attention mechanism to enhance the algorithm’s performance. Subsequently, simulation experiments were conducted to compare the improved algorithm with an algorithm based on a classification model and an algorithm based on the traditional translation model using open corpus data and English translations from freshmen. The results demonstrated that the optimized algorithm yielded superior intuitive error correction outcomes. When applied to both the open corpus and the English translations of college freshmen, the optimized error correction algorithm outperformed the others. The traditional translation model-based algorithm came in second, while the classification model-based algorithm showed the least favorable performance. Furthermore, all three error correction algorithms experienced a decrease in performance when dealing with English compositions from freshmen. However, the optimized algorithm exhibited a relatively smaller decline.