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
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.eacl-main.315.pdf