Performance Evaluation and Improvement of Deep Echo State Network Models in English Writing Assistance and Grammar Error Correctionn

Abstract

INTRODUCTION: The research on the performance evaluation model of English writing tutoring and grammar error correction is very necessary, which is not only conducive to the rational allocation of teachers' writing tutoring resources, but also more conducive to the timely and effective correction of students' grammatical errors.OBJCTIVES: Aiming at the problems of non-specific quantification, low precision, and low real-time performance evaluation methods for English writing grammar error correction in current methods.METHODS: This paper proposes a grammar error correction performance evaluation method based on deep echo state network with gold rush optimisation algorithm. Firstly, by analysing the process of English writing assistance and grammatical error correction, we extract the evaluation features of grammatical error correction type and construct the performance evaluation system; then, we improve the deep confidence network through the gold rush optimization algorithm and construct the grammatical error correction performance evaluation model; finally, we analyse it through simulation experiments.RESULTS: The results show that the proposed method improves the evaluation accuracy, robustness. The absolute value of the relative error of the evaluation value of the syntactic error correction performance of the method is controlled within the range of 0.02.CONCLUSION: The problems of non-specific quantification, low precision and low real-time performance of the application of English writing grammar error correction performance assessment methods are solved.