mlsanityData to 7 Oct 2026

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

arXiv 2101.05972 ↗First posted 15 Jan 2021cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 15 Jan 2021.

Builds on

5 of 14 references.

References · 5

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  3. 2018Graph Convolution over Pruned Dependency Trees Improves Relation Extractioncited by 146
  4. 2017Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelingcited by 137
  5. 2019Fine-Grained Spoiler Detection from Large-Scale Review Corporacited by 76

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1.

5 papers.