In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model is constructed. Secondly, the text reader is designed, and the deep belief neural network is introduced to predict the question answering probability. Finally, the popular English dataset of SQuAD is used for test analysis. The final results show that, after the comparative analysis of different learning methods, it is found that the English multitext reading comprehension model has a strong reading comprehension ability. In addition, two evaluation methods are used to score the overall performance of the model, which shows that the overall score of the English multitext reading comprehension model based on the deep confidence neural network is more than 90, and the efficiency will not be reduced because of the change of the number of documents in the dataset. The above results show that the use of the deep belief neural network to improve the probability generation performance of the model can well solve the task of English multitext reading comprehension, effectively reduce the difficulty of machine reading comprehension in multitask reading, and has a good guiding significance for promoting human convenient Internet knowledge acquisition.

中文翻译：

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基于深度置信神经网络的英语多文本阅读理解模型分析

为了解决机器阅读理解中答案预测准确率低、效率低的问题，提出一种基于深度置信神经网络的多文本英语阅读理解模型。首先，构建多文本阅读理解模型中的段落选择器。其次，设计了文本阅读器，并引入深度置信神经网络来预测回答概率。最后使用流行的英文数据集SQuAD进行测试分析。最终结果表明，经过不同学习方法的对比分析，发现英语多文本阅读理解模型具有很强的阅读理解能力。此外，还使用两种评估方法对模型的整体性能进行评分，由此可见，基于深度置信神经网络的英语多文本阅读理解模型总体得分在90分以上，并且不会因为数据集中文档数量的变化而降低效率。以上结果表明，利用深度置信神经网络提高模型的概率生成性能，能够很好地解决英语多文本阅读理解任务，有效降低机器阅读理解在多任务阅读中的难度，具有良好的指导作用。对于促进人类便捷的互联网知识获取具有重要意义。