@inproceedings{li-tuzhilin-2019-towards,
title = "Towards Controllable and Personalized Review Generation",
author = "Li, Pan and
Tuzhilin, Alexander",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1319",
doi = "10.18653/v1/D19-1319",
pages = "3237--3245",
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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%0 Conference Proceedings
%T Towards Controllable and Personalized Review Generation
%A Li, Pan
%A Tuzhilin, Alexander
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F li-tuzhilin-2019-towards
%X 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.
%R 10.18653/v1/D19-1319
%U https://aclanthology.org/D19-1319
%U https://doi.org/10.18653/v1/D19-1319
%P 3237-3245
Markdown (Informal)
[Towards Controllable and Personalized Review Generation](https://aclanthology.org/D19-1319) (Li & Tuzhilin, EMNLP-IJCNLP 2019)
ACL
- Pan Li and Alexander Tuzhilin. 2019. Towards Controllable and Personalized Review Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3237–3245, Hong Kong, China. Association for Computational Linguistics.