Hundreds of thousands of mobile app users post their reviews online. Responding to user reviews promptly and satisfactorily improves application ratings, which is key to application popularity and success. The proliferation of such reviews makes it virtually impossible for developers to keep up with responding manually. To address this challenge, recent work has shown the possibility of automatic response generation by training a seq2seq model with a large collection of review-response pairs. However, because the training review-response pairs are aggregated from many different apps, it remains challenging for such models to generate app-specific responses, which, on the other hand, are often desirable as appwes have different features and concerns. Solving the challenge by simply building an app-specific generative model per app (i.e., training the model with review-response pairs of a single app) may be insufficient because individual apps have limited review-response pairs, and such pairs typically lack the relevant information needed to respond to a new review.To enable app-specific response generation, this work proposes AARSYNTH: an app-aware response synthesis system. The key idea behind AARSYNTH is to augment the seq2seq model with information specific to a given app. Given a new user review, AARSYNTH first retrieves the top-K most relevant app reviews and the most relevant snippet from the app description. The retrieved information and the new user review are then fed into a fused machine learning model that integrates the seq2seq model with a machine reading comprehension model. The latter helps digest the retrieved reviews and app description. Finally, the fused model generates a response that is customized to the given app. We evaluated AARSYNTH using a large corpus of reviews and responses from Google Play. The results show that AARSYNTH outperforms the state-of-the-art system by 22.2% on BLEU-4 score. Furthermore, our human study shows that AARSYNTH produces a statistically significant improvement in response quality compared to the state-of-the-art system.
|Title of host publication||Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020|
|Editors||Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz|
|Number of pages||10|
|State||Published - Dec 10 2020|
|Event||8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States|
Duration: Dec 10 2020 → Dec 13 2020
|Name||Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020|
|Conference||8th IEEE International Conference on Big Data, Big Data 2020|
|Period||12/10/20 → 12/13/20|
Bibliographical noteFunding Information:
This work is supported in part by the National Science Foundation under grants IIS-1838222 and IIS-1901379.
© 2020 IEEE.
- App reviews
- Machine translation
- Natural language generation
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
- Safety, Risk, Reliability and Quality