Chinese machine reading comprehension based on deep learning neural network.

Most of the existing machine reading comprehension (MRC) models mainly learn the attention mechanism of interactive alignment between questions and texts, and there are still great difficulties in the characterisation of ultra-long texts. The complex grammar, diverse expressions, and flexible structure of the Chinese corpus make the MRC task even more difficult. In this paper, a transformer-based neural network model extremely brisk and clever net (XBCNet) was proposed for the MRC problem of Chinese text. XBCNet expands the receptive field by introducing a stack of dilated convolution neural networks, and it is also closely coupled with the word vector algorithm. This process aims to reduce the training and inference time of the model, while alleviating the drawback of long questions that are difficult to answer accurately. The experimental results demonstrate that XBCNet improves the performance of MRC under general texts and obtains the best inference effect in limited computing resources.