Multi-scenario application of Chatgpt-based language modeling for empowering English language teaching and learning

Abstract

Abstract This paper discusses the multi-scenario application of ChatGPT-based language modeling in English language teaching, and empirical experiments are conducted to support the research findings. The study includes constructing and analyzing English composition scoring and similarity detection models. The BERT-BiLSTM algorithm was utilized and compared to the Word2Vec-BiLSTM model. The BERT-BiLSTM-based English composition scoring model has a high correlation and consistency with the original scores, with an average correlation of 0.72 and a consistency of 82%. Conversely, the Word2Vec-BiLSTM model has a lesser correlation and consistency. We created a model and used different K values for the experiment to detect English composition similarity. The correctness, recall, and F1 measures were higher at a K value 200, with F1 values fluctuating between 89.35% and 95.14%. These support the high accuracy and efficiency of ChatGPT-based language modeling in English language teaching.