Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks

Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, Qifa Ke


Abstract
Question generation, the task of automatically creating questions that can be answered by a certain span of text within a given passage, is important for question-answering and conversational systems in digital assistants such as Alexa, Cortana, Google Assistant and Siri. Recent sequence to sequence neural models have outperformed previous rule-based systems. Existing models mainly focused on using one or two sentences as the input. Long text has posed challenges for sequence to sequence neural models in question generation – worse performances were reported if using the whole paragraph (with multiple sentences) as the input. In reality, however, it often requires the whole paragraph as context in order to generate high quality questions. In this paper, we propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation. With sentence-level inputs, our model outperforms previous approaches with either sentence-level or paragraph-level inputs. Furthermore, our model can effectively utilize paragraphs as inputs, pushing the state-of-the-art result from 13.9 to 16.3 (BLEU_4).
Anthology ID:
D18-1424
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3901–3910
Language:
URL:
https://aclanthology.org/D18-1424
DOI:
10.18653/v1/D18-1424
Bibkey:
Cite (ACL):
Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. 2018. Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3901–3910, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks (Zhao et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1424.pdf
Data
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