Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations

Jianmo Ni, Julian McAuley


Abstract
In this paper, we focus on the problem of building assistive systems that can help users to write reviews. We cast this problem using an encoder-decoder framework that generates personalized reviews by expanding short phrases (e.g. review summaries, product titles) provided as input to the system. We incorporate aspect-level information via an aspect encoder that learns aspect-aware user and item representations. An attention fusion layer is applied to control generation by attending on the outputs of multiple encoders. Experimental results show that our model successfully learns representations capable of generating coherent and diverse reviews. In addition, the learned aspect-aware representations discover those aspects that users are more inclined to discuss and bias the generated text toward their personalized aspect preferences.
Anthology ID:
P18-2112
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
706–711
URL:
https://www.aclweb.org/anthology/P18-2112
DOI:
10.18653/v1/P18-2112
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PDF:
https://www.aclweb.org/anthology/P18-2112.pdf
Video:
 https://vimeo.com/285806016
Presentation:
 P18-2112.Presentation.pdf