MG-PriFair

• Multimodal Review Generation with Privacy and Fairness Awareness

Model architecture

Datasets

Citation

@inproceedings{vu:mg-prifair:2020,
    title = "Multimodal Review Generation with Privacy and Fairness Awareness",
    author = "Xuan-Son Vu, Thanh-Son Nguyen, Duc-Trong Le, Lili Jiang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    abstract = "Users express their opinions towards entities (e.g., restaurants) via online reviews which can be
in diverse forms such as text, ratings, and images. Modeling reviews are advantageous for user
behavior understanding which, in turn, supports various user-oriented tasks such as recommendation,
sentiment analysis, and review generation. In this paper, we propose MG-PriFair, a multimodal
neural-based framework, which generates personalized reviews with privacy and fairness
awareness. Motivated by the fact that reviews might contain personal information and sentiment
bias, we propose a novel differentially private (dp)-embedding model for training privacy guaranteed
embeddings and an evaluation approach for sentiment fairness in the food-review domain.
Experiments on our novel review dataset show that MG-PriFair is capable of generating plausibly
long reviews while controlling the amount of exploited user data and using the least sentimentbiased
word embeddings. To the best of our knowledge, we are the first to bring user privacy and
sentiment fairness into the review generation task. The dataset and source codes are available at
https://github.com/ReML-AI/MG-PriFair.",
}