Towards Controllable and Personalized Review Generation
arXiv:1910.03506
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.
Accepted to EMNLP 2019
References in corpus (7)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Sequence to Sequence Learning with Neural Networks
- Conditional Generative Adversarial Nets
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- A Hierarchical Neural Autoencoder for Paragraphs and Documents
- Long Text Generation via Adversarial Training with Leaked Information
- Context-aware Natural Language Generation with Recurrent Neural Networks