Analysis of the Application of Feedback Filtering and Seq2Seq Model in English Grammar

Abstract

Natural language processing (NLP) technology is widely used in grammatical error correction, but its error correction logic is complex and fault-tolerant, which leads to low accuracy. With the progress of deep learning and big data analysis technology, a new method is proposed in the technical means of English grammar error correction. This paper proposes a deep learning model-based feedback grammar error correction method, which can effectively improve the accuracy and tolerance of grammar error correction. Firstly, the Seq2Seq model with attention mechanism is proposed, and then the feedback filtering model is integrated, so that the existing errors or inefficient grammars can be corrected again, thus improving the efficiency of the model. Through a large number of text detection, the model proposed in this paper has high execution efficiency and application ability and can widely meet the needs of English translation and grammar correction.