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"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism

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arxiv 2101.05972 v1 pith:KLT7FCSE submitted 2021-01-15 cs.CL

classification cs.CL
keywords spoilerdetectiondependencymodelsgraphmodelnetworksneural
verification ladder T0 review T1 audit T2 compute T3 formal

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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Unveiling the Hidden: Movie Genre and User Bias in Spoiler Detection

    cs.IR 2025-04 conditional novelty 6.0 of 10

    GUSD claims state-of-the-art spoiler detection on IMDb datasets using genre-aware routing and user-bias features from dynamic graph pretraining.

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