Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums

Zi Chai, Xinyu Xing, Xiaojun Wan, Bo Huang


Abstract
Teaching machines to ask questions is an important yet challenging task. Most prior work focused on generating questions with fixed answers. As contents are highly limited by given answers, these questions are often not worth discussing. In this paper, we take the first step on teaching machines to ask open-answered questions from real-world news for open discussion (openQG). To generate high-qualified questions, effective ways for question evaluation are required. We take the perspective that the more answers a question receives, the better it is for open discussion, and analyze how language use affects the number of answers. Compared with other factors, e.g. topic and post time, linguistic factors keep our evaluation from being domain-specific. We carefully perform variable control on 11.5M questions from online forums to get a dataset, OQRanD, and further perform question analysis. Based on these conclusions, several models are built for question evaluation. For openQG task, we construct OQGenD, the first dataset as far as we know, and propose a model based on conditional generative adversarial networks and our question evaluation model. Experiments show that our model can generate questions with higher quality compared with commonly-used text generation methods.
Anthology ID:
P19-1497
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5032–5046
Language:
URL:
https://aclanthology.org/P19-1497
DOI:
10.18653/v1/P19-1497
Bibkey:
Cite (ACL):
Zi Chai, Xinyu Xing, Xiaojun Wan, and Bo Huang. 2019. Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5032–5046, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums (Chai et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1497.pdf
Video:
 https://vimeo.com/385272792
Code
 ChaiZ-pku/OQRanD-and-OQGenD
Data
OQGendOQRanD