Superimposed Attention Mechanism-Based CNN Network for Reading Comprehension and Question Answering

Mingqi Li; Xuefei Hou; Jiaoe Li; Kai Gao

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Superimposed Attention Mechanism-Based CNN Network for Reading Comprehension and Question Answering

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摘要

cqvip:In recent years, end-to-end models have been widely used in the fields of machine comprehension (MC) and question answering (QA). Recurrent neural network (RNN) or convolutional neural network (CNN) is combined with attention mechanism to construct models to improve their accuracy. However, a single attention mechanism does not fully express the meaning of the text. In this paper, recurrent neural network is replaced with the convolutional neural network to process the text, and a superimposed attention mechanism is proposed. The model was constructed by combining a convolutional neural network with a superimposed attention mechanism. It shows that good results are achieved on the Stanford question answering dataset (SQuAD).

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来源
《国际计算机前沿大会会议论文集》 |2019年第2期|P.35-37|共3页
作者
Mingqi Li; Xuefei Hou; Jiaoe Li; Kai Gao;
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作者单位

Princeton University Princeton NJ 08544 USA;

School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang 050700 China;

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原文格式 PDF
正文语种 chi
中图分类社会科学总论;
关键词
Convolutional neural network; Attention mechanism; Machine comprehension; Question answering;

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Superimposed Attention Mechanism-Based CNN Network for Reading Comprehension and Question Answering

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