GUSD claims state-of-the-art spoiler detection on IMDb datasets using genre-aware routing and user-bias features from dynamic graph pretraining.
"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
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abstract
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites. Although dependency relations between context words are important for detecting spoilers, current attention-based spoiler detection models are insufficient for utilizing dependency relations. To address this problem, we propose a new spoiler detection model called SDGNN that is based on syntax-aware graph neural networks. In the experiments on two real-world benchmark datasets, we show that our SDGNN outperforms the existing spoiler detection models.
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Unveiling the Hidden: Movie Genre and User Bias in Spoiler Detection
GUSD claims state-of-the-art spoiler detection on IMDb datasets using genre-aware routing and user-bias features from dynamic graph pretraining.