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Teaching machines to ask questions

Yao, Kaichun; Zhang, Libo; Luo, Tiejian; Tao, Lili; Wu, Yanjun

Authors

Kaichun Yao

Libo Zhang

Tiejian Luo

Yanjun Wu



Abstract

We propose a novel neural network model that aims to generate diverse and human-like natural language questions. Our model not only directly captures the variability in possible questions by using a latent variable, but also generates certain types of questions by introducing an additional observed variable. We deploy our model in the generative adversarial network (GAN) framework and modify the discriminator which not only allows evaluating the question authenticity, but predicts the question type. Our model is trained and evaluated on a question-answering dataset SQuAD, and the experimental results shown the proposed model is able to generate diverse and readable questions with the specific attribute.

Presentation Conference Type Conference Paper (unpublished)
Start Date Jul 13, 2018
Publication Date Jul 1, 2018
Peer Reviewed Peer Reviewed
Pages 4546-4552
Book Title Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
ISBN 9780999241127
APA6 Citation Yao, K., Zhang, L., Luo, T., Tao, L., & Wu, Y. (2018, July). Teaching machines to ask questions. Paper presented at International Joint Conferences on Artificial Intelligence Organization
Publisher URL https://doi.org/10.24963/ijcai.2018/632
Additional Information Title of Conference or Conference Proceedings : Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence