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.
Original language | English |
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Title of host publication | EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 3613-3617 |
Number of pages | 5 |
ISBN (Electronic) | 9781954085022 |
Publication status | Published - 2021 |
Event | 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 - Virtual, Online Duration: 2021 Apr 19 → 2021 Apr 23 |
Publication series
Name | EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference |
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Conference
Conference | 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 |
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City | Virtual, Online |
Period | 21/4/19 → 21/4/23 |
Bibliographical note
Funding Information:This research was supported by National Research Foundation of Korea (NRF-2020R1A2C3010638). This research was also supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience program (IITP-2020-0-01819) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).
Publisher Copyright:
© 2021 Association for Computational Linguistics
ASJC Scopus subject areas
- Software
- Computational Theory and Mathematics
- Linguistics and Language