MMoE: Robust Spoiler Detection with Multi-modal Information and Domain-aware Mixture-of-Experts
This addresses the problem of spoilers degrading the movie-watching experience for users of online review platforms, representing an incremental advance by integrating multi-modal information and domain-aware techniques.
The paper tackled spoiler detection in online movie reviews by proposing MMoE, a multi-modal network using graph, text, and metadata features with a Mixture-of-Experts architecture for domain generalization, achieving state-of-the-art performance with accuracy and F1-score improvements of 2.56% and 8.41% on two datasets.
Online movie review websites are valuable for information and discussion about movies. However, the massive spoiler reviews detract from the movie-watching experience, making spoiler detection an important task. Previous methods simply focus on reviews' text content, ignoring the heterogeneity of information in the platform. For instance, the metadata and the corresponding user's information of a review could be helpful. Besides, the spoiler language of movie reviews tends to be genre-specific, thus posing a domain generalization challenge for existing methods. To this end, we propose MMoE, a multi-modal network that utilizes information from multiple modalities to facilitate robust spoiler detection and adopts Mixture-of-Experts to enhance domain generalization. MMoE first extracts graph, text, and meta feature from the user-movie network, the review's textual content, and the review's metadata respectively. To handle genre-specific spoilers, we then adopt Mixture-of-Experts architecture to process information in three modalities to promote robustness. Finally, we use an expert fusion layer to integrate the features from different perspectives and make predictions based on the fused embedding. Experiments demonstrate that MMoE achieves state-of-the-art performance on two widely-used spoiler detection datasets, surpassing previous SOTA methods by 2.56% and 8.41% in terms of accuracy and F1-score. Further experiments also demonstrate MMoE's superiority in robustness and generalization. Our code is available at https://github.com/zzqbjt/Spoiler-Detection.