A Novel Chinese Reading Comprehension Model Based on Attention Mechanism and Convolutional Neural Networks

This paper presents a novel machine reading comprehension model based on deep learning techniques in Chinese environment. In our manner, the training process can be performed using a general-level GPU, and the convergence of the training process can be accelerated for a shorter period of time. In the architectural design, two main constituting parts include Self-Attention Mechanism and Convolutional Neural Networks. To enhance the interaction between an article and questions, we carry out the operation of Context-Query Attention twice, so that our model is more effectively for acquiring the information of the questions related to the article and converges faster while training. In the experiment, we adopt the Delta Reading Comprehension Dataset for model evaluation in Chinese environment. The experimental results reveal that our model is able to reach the accuracy of 64.9% for EM and 79.0% for Fl. The convergence time is less than 1 hour using the Titan XP GPU, and the memory usage is comparatively lower. The training performance is about 3 times faster than other models with state- of-the-art architecture.