Towards Controllable and Personalized Review Generation

Pan Li; Alexander Tuzhilin

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arXiv · 1910.03506

Towards Controllable and Personalized Review Generation

Pan Li·Alexander Tuzhilin

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Abstract

In this paper, we propose a novel model RevGAN that automatically generates controllable and personalized user reviews based on the arbitrarily given sentimental and stylistic information. RevGAN utilizes the combination of three novel components, including self-attentive recursive autoencoders, conditional discriminators, and personalized decoders. We test its performance on the several real-world datasets, where our model significantly outperforms state-of-the-art generation models in terms of sentence quality, coherence, personalization and human evaluations. We also empirically show that the generated reviews could not be easily distinguished from the organically produced reviews and that they follow the same statistical linguistics laws.

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Pan Li, Alexander Tuzhilin. 2019-09-30. Towards Controllable and Personalized Review Generation. https://arxiv.org/abs/1910.03506

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