Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks (D18-1)
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| Challenge: | Existing rule-based question generation models rely on one or two sentences as input, while long text has posed challenges for sequence to sequence neural models. |
| Approach: | They propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation. |
| Outcome: | The proposed model outperforms existing models with sentence-level or paragraph-level inputs pushing the state-of-the-art result from 13.9 to 16.3 (BLEU_4). |
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Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
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