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            <namePart type="given">Dan</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Review-based Question Generation with Adaptive Instance Transfer and Augmentation
%A Yu, Qian
%A Bing, Lidong
%A Zhang, Qiong
%A Lam, Wai
%A Si, Luo
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F yu-etal-2020-review
%X 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.
%R 10.18653/v1/2020.acl-main.26
%U https://aclanthology.org/2020.acl-main.26/
%U https://doi.org/10.18653/v1/2020.acl-main.26
%P 280-290

Markdown (Informal)

[Review-based Question Generation with Adaptive Instance Transfer and Augmentation](https://aclanthology.org/2020.acl-main.26/) (Yu et al., ACL 2020)

• Review-based Question Generation with Adaptive Instance Transfer and Augmentation (Yu et al., ACL 2020)

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.