Bidirectional Deep Learning Model based on Attention Mechanism in English Cloze Tests

Abstract

English cloze tests, as a common form of language assessment, aim to evaluate learners' comprehensive understanding of context, vocabulary, and grammar. However, the complex contextual relationships and long-distance dependencies within the questions pose significant challenges for machine learning models. Traditional rule-based or statistical methods struggle to effectively capture the intricate contextual information present in sentences. This study aims to develop a deep learning-based English cloze test answering system to enhance students' ability to tackle such questions. To address the limitations of traditional methods in handling complex contexts and long-distance dependencies, a model that combines bidirectional gated recurrent units (BiGRU) and attention mechanisms is proposed. This model is better equipped to capture the surrounding context of sentences and dynamically adjust attention to accurately predict the missing words. Additionally, integrating the embedding layer with BiGRU and attention mechanisms further improves model performance. Testing results based on the Children’s Book Test dataset are highly promising. Our model excels in key metrics such as accuracy, recall, F1 score, and Cohen's Kappa, achieving scores of 77.5%, 77.5%, 0.758, and 0.7649, respectively. Compared to traditional models, our approach demonstrates clear advantages in handling complex contexts and long sentences. This research provides new technical support for developing more intelligent English learning systems.