A Novel Machine Reading Comprehension Approach Through Attention Mechanism-Based BERT Model

This study conducted an in-depth analysis and understanding of the Bidirectional Encoder Representation from Transformers (BERT) model, utilizing its powerful bidirectional encoding ability to provide a rich text representation foundation for machine reading comprehension (MRC) tasks. Subsequently, an attention-based BERT model was designed. The attention mechanism allows the model to dynamically focus on different parts of the input sequence when processing text, capturing key information and ignoring irrelevant content. By combining the attention mechanism with the BERT model and adjusting attention weights, the model can focus on key sentences and vocabulary in the text. To verify the effectiveness of the model, a large number of experiments were conducted in this study. The experiment selected SQuAD and TriviaQA MRC datasets, and based on the experimental results, this study optimized the model. By adjusting model parameters, improving the implementation of attention mechanisms, and introducing additional training techniques, the optimized model achieved significantly better results than traditional methods on multiple datasets, especially when dealing with long texts and problems that require deep reasoning, demonstrating strong comprehension and accurate answer generation abilities. In the future, this research will continue to delve into the internal mechanisms of the model, explore more effective optimization strategies, and attempt to extend this method to other natural language processing tasks, providing strong support for the comprehensive development of natural language processing technology.