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

Buru Chang, Inggeol Lee, Hyunjae Kim, Jaewoo Kang


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.
Anthology ID:
2021.eacl-main.315
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3613–3617
Language:
URL:
https://aclanthology.org/2021.eacl-main.315
DOI:
10.18653/v1/2021.eacl-main.315
Bibkey:
Cite (ACL):
Buru Chang, Inggeol Lee, Hyunjae Kim, and Jaewoo Kang. 2021. “Killing Me” Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3613–3617, Online. Association for Computational Linguistics.
Cite (Informal):
“Killing Me” Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism (Chang et al., EACL 2021)
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PDF:
https://aclanthology.org/2021.eacl-main.315.pdf