“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

“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^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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
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

Conference

Conference	16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021
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

Cite this

Chang, B., Lee, I., Kim, H., & Kang, J. (2021). “Killing me” is not a spoiler: Spoiler detection model using graph neural networks with dependency relation-aware attention mechanism. In EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 3613-3617). (EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference). Association for Computational Linguistics (ACL).

“Killing me” is not a spoiler: Spoiler detection model using graph neural networks with dependency relation-aware attention mechanism. / Chang, Buru; Lee, Inggeol; Kim, Hyunjae et al.
EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2021. p. 3613-3617 (EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chang, B, Lee, I, Kim, H & Kang, J 2021, “Killing me” is not a spoiler: Spoiler detection model using graph neural networks with dependency relation-aware attention mechanism. in EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, Association for Computational Linguistics (ACL), pp. 3613-3617, 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021, Virtual, Online, 21/4/19.

Chang B, Lee I, Kim H, Kang J. “Killing me” is not a spoiler: Spoiler detection model using graph neural networks with dependency relation-aware attention mechanism. In EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2021. p. 3613-3617. (EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference).

Chang, Buru ; Lee, Inggeol ; Kim, Hyunjae et al. / “Killing me” is not a spoiler : Spoiler detection model using graph neural networks with dependency relation-aware attention mechanism. EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2021. pp. 3613-3617 (EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference).

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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.",

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“Killing me” is not a spoiler: Spoiler detection model using graph neural networks with dependency relation-aware attention mechanism

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

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