Optimization of QANet Model Based on Cross-Entropy and Information Entropy Constraints and Its Application to Machine Reading Comprehension

机器阅读理解(MRC)作为自然语言处理(NLP)领域的重要任务，旨在使机器能够准确理解人类语言并回答相关问题。本文聚焦于QANet模型的优化研究，该模型融合了卷积神经网络(CNN)和自注意力机制，以实现对文本的精准理解和答案定位。传统QANet模型依赖单一交叉熵损失函数进行训练，可能导致答案分布不确定性增加。为此，本文提出一种结合交叉熵与信息熵的混合损失函数，通过引入信息熵约束项，在提升模型准确性的同时增强概率分布的置信度。实验结果表明，改进后的模型在SQuAD数据集上的F1得分和精确匹配(EM)均有所提升，为MRC任务中的损失函数设计提供了新的优化方向。Machine Reading Comprehension (MRC), as a crucial task in the Natural Language Processing (NLP) field, aims to enable machines to accurately understand human language and answer related questions. This paper focuses on the optimization study of the QANet model, which integrates Convolutional Neural Networks (CNN) and self-attention mechanisms to achieve precise text comprehension and answer localization. Traditional QANet models rely solely on a single cross-entropy loss function for training, which may increase uncertainty in answer distribution. To address this, we propose a hybrid loss function combining cross-entropy and information entropy. By introducing an information entropy constraint term, this approach enhances model accuracy while strengthening the confidence of probability distributions. Experimental results demonstrate that the improved model achieves higher F1 scores and Exact Match (EM) metrics on the SQuAD dataset, providing a new optimization direction for loss function design in MRC tasks.