Kaichun Yao
Teaching machines to ask questions
Yao, Kaichun; Zhang, Libo; Luo, Tiejian; Tao, Lili; Wu, Yanjun
Authors
Libo Zhang
Tiejian Luo
Lili Tao
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.
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, Stockholm, Sweden
Presentation Conference Type | Conference Paper (unpublished) |
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Conference Name | International Joint Conferences on Artificial Intelligence Organization |
Conference Location | Stockholm, Sweden |
Start Date | Jul 13, 2018 |
End Date | Jul 19, 2018 |
Acceptance Date | Apr 16, 2018 |
Publication Date | Jul 1, 2018 |
Deposit Date | Jul 17, 2018 |
Peer Reviewed | Peer Reviewed |
Pages | 4546-4552 |
Book Title | Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence |
ISBN | 9780999241127 |
Public URL | https://uwe-repository.worktribe.com/output/865419 |
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 |