基于多尺度上下文的英文作文自动评分研究OA北大核心CSTPCD

Presently,the automatic scoring model for essays lacks extraction of semantic features from different context scales,and fails to calculate the degree of correlation between the topic of the essay from the sentence level.This study proposes a method MSC for automatic scoring of English esssay based on a multi-scale context.The method uses an XLNet English pre-training model to extract word and sentence embeddings from the original essay text,accurately captures vector embeddings that match the context when processing long sequence texts,improves the quality of dynamic vector semantic representation,addresses the problem of polysemy,and extracts phrase level embeddings at different scales through a one-dimensional convolution module.The MSC network captures high-dimensional latent contextual semantic associations at the word,phrase,and sentence levels by combining Built-in Self-Attention Simple Recurrence Units(BSASRU)and global attention mechanisms.It uses sentence vectors to calculate semantic similarity with the essay topic and extracts topic level features.All features are input into the fusion layer and are automatically graded through a linear layer.The experimental results on the publicly available standard English essay scoring dataset ASAP demonstrate that the MSC model achieves an average Quadratic Weighted Kappa(QWK)value of 80.5%.Moreover,it achieves the best performance on multiple subsets,outperforming the deep learning automatic scoring model in experimental comparison,thereby proving its effectiveness in English essay automatic scoring tasks.