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Tan, M., et al. (2015) LSTM-Based Deep Learning Models for Non-Factoid Answer Selection. arXiv:1511.04108

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• TITLE: Research Progress of Automatic Question Answering System Based on Deep Learning

  AUTHORS: Shiyao Zhao, Zhezhi Jin

  KEYWORDS: Natural Language Processing, Deep Learning, Machine Reading Comprehension, Automatic Question Answering, Attention Mechanism

  JOURNAL NAME: Open Access Library Journal, Vol.7 No.6, June 17, 2020

  ABSTRACT: With the rapid development of deep learning, a large number of machine reading comprehension models based on deep learning have emerged. Firstly, the paper points out the shortcomings of traditional search engines and explains the advantages of automatic question answering systems compared with them. Secondly, it summarizes the development process of the deep learning-based machine reading comprehension model, and expounds the overall framework and operation principle of the model, as well as the advantages and application scope of the model. Finally, it points out where the development trend lies, and lays the foundation for follow-up researchers.