Towards Controllable and Personalized Review Generation
This addresses the need for automated, customizable review generation for applications like e-commerce and content creation, though it appears incremental as it builds on existing generative models.
The paper tackled the problem of generating user reviews with control over sentiment and style, resulting in a model that significantly outperforms state-of-the-art methods in quality, coherence, and personalization, as shown in human evaluations and statistical tests.
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.