A machine reading comprehension method based on threshold convolution neural network

Abstract

The invention provides a machine reading comprehension method based on a threshold convolution neural network. The method comprises the following steps: constructing a threshold convolution neural network model, comprising an input layer, a threshold convolution layer and an answer layer; The input layer is used for encoding the target article, and the encoded article vector sequence, the questionvector sequence and the answer vector sequence are transmitted to the threshold convolution layer. The threshold convolution layer generates text, question and answer expressions with high-level semantic information through interactive mode, and transmits these expressions to the answer layer. Finally, the answer layer makes reasoning decision and makes prediction. Determine the target article, import the threshold convolution neural network model for machine reading comprehension, and derive the prediction results. The machine reading comprehension method based on the threshold convolution neural network provided by the invention effectively simplifies the neural network model, greatly reduces the training and testing time, improves the processing efficiency, and improves the sense of user experience. Keep the long-term dependency of the text and accurately predict the answer information.

Description

technical field [0001] The present invention relates to the field of natural language processing, and more specifically, relates to a machine reading comprehension method based on a threshold convolutional neural network. Background technique [0002] The goal of machine reading comprehension is to teach machines to learn to read and understand human language. This is a long-term goal in the field of natural language processing. Its task forms mainly include cloze reading comprehension, paragraph extraction reading comprehension, and open domain reading comprehension. When we give a paragraph, a question, and several candidate answers, the machine can reason based on the given paragraph and question, combined with common sense knowledge, to get the final answer. The current mainstream models on this task are all based on the traditional loop structure. Although the result can learn the long-term dependence of the text, which is conducive to promoting the model to reason, but...

AI Technical Summary

Problems solved by technology

[0003] The present invention provides a machine reading comprehension method based on a threshold convolutional neural network in order to overcome the above-mentioned technical defects of high complexity of existing models, long time consumption, impact on user experience, and unfavorable research development

Images