Vu, Xuan Son and Nguyen, Thanh Son and Le, Duc Trong and Jiang, Lili
(2020)
Multimodal Review Generation with Privacy and Fairness Awareness.
In: Proceedings of the 28th International Conference on Computational Linguistics.
Abstract
Users express their opinions towards entities (e.g., restaurants) via online reviews which can bein diverse forms such as text, ratings, and images. Modeling reviews are advantageous for userbehavior understanding which, in turn, supports various user-oriented tasks such as recommen-dation, sentiment analysis, and review generation. In this paper, we propose MG-PriFair, a multi-modal neural-based framework, which generates personalized reviews with privacy and fairnessawareness. Motivated by the fact that reviews might contain personal information and sentimentbias, we propose a novel differentially private (dp)-embedding model for training privacy guar-anteed 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 plausiblylong reviews while controlling the amount of exploited user data and using the least sentiment-biased word embeddings. To the best of our knowledge, we are the first to bring user privacy andsentiment fairness into the review generation task. The dataset and source codes are available athttps://github.com/ReML-AI/MG-PriFair.
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