Review-based Question Generation with Adaptive Instance Transfer and Augmentation

Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si


Abstract
While online reviews of products and services become an important information source, it remains inefficient for potential consumers to exploit verbose reviews for fulfilling their information need. We propose to explore question generation as a new way of review information exploitation, namely generating questions that can be answered by the corresponding review sentences. One major challenge of this generation task is the lack of training data, i.e. explicit mapping relation between the user-posed questions and review sentences. To obtain proper training instances for the generation model, we propose an iterative learning framework with adaptive instance transfer and augmentation. To generate to the point questions about the major aspects in reviews, related features extracted in an unsupervised manner are incorporated without the burden of aspect annotation. Experiments on data from various categories of a popular E-commerce site demonstrate the effectiveness of the framework, as well as the potentials of the proposed review-based question generation task.
Anthology ID:
2020.acl-main.26
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
280–290
Language:
URL:
https://aclanthology.org/2020.acl-main.26
DOI:
10.18653/v1/2020.acl-main.26
Bibkey:
Cite (ACL):
Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, and Luo Si. 2020. Review-based Question Generation with Adaptive Instance Transfer and Augmentation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 280–290, Online. Association for Computational Linguistics.
Cite (Informal):
Review-based Question Generation with Adaptive Instance Transfer and Augmentation (Yu et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.26.pdf
Video:
 http://slideslive.com/38929281