@inproceedings{tran-etal-2023-spoiler,
title = "Spoiler Detection as Semantic Text Matching",
author = "Tran, Ryan and
Xu, Canwen and
McAuley, Julian",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.373",
doi = "10.18653/v1/2023.emnlp-main.373",
pages = "6109--6113",
abstract = "Engaging with discussion of TV shows online often requires individuals to refrain from consuming show-related content for extended periods to avoid spoilers. While existing research on spoiler detection shows promising results in safeguarding viewers from general spoilers, it fails to address the issue of users abstaining from show-related content during their watch. This is primarily because the definition of a spoiler varies depending on the viewer{'}s progress in the show, and conventional spoiler detection methods lack the granularity to capture this complexity. To tackle this challenge, we propose the task of spoiler matching, which involves assigning an episode number to a spoiler given a specific TV show. We frame this task as semantic text matching and introduce a dataset comprised of comments and episode summaries to evaluate model performance. Given the length of each example, our dataset can also serve as a benchmark for long-range language models.",
}
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<abstract>Engaging with discussion of TV shows online often requires individuals to refrain from consuming show-related content for extended periods to avoid spoilers. While existing research on spoiler detection shows promising results in safeguarding viewers from general spoilers, it fails to address the issue of users abstaining from show-related content during their watch. This is primarily because the definition of a spoiler varies depending on the viewer’s progress in the show, and conventional spoiler detection methods lack the granularity to capture this complexity. To tackle this challenge, we propose the task of spoiler matching, which involves assigning an episode number to a spoiler given a specific TV show. We frame this task as semantic text matching and introduce a dataset comprised of comments and episode summaries to evaluate model performance. Given the length of each example, our dataset can also serve as a benchmark for long-range language models.</abstract>
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%0 Conference Proceedings
%T Spoiler Detection as Semantic Text Matching
%A Tran, Ryan
%A Xu, Canwen
%A McAuley, Julian
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F tran-etal-2023-spoiler
%X Engaging with discussion of TV shows online often requires individuals to refrain from consuming show-related content for extended periods to avoid spoilers. While existing research on spoiler detection shows promising results in safeguarding viewers from general spoilers, it fails to address the issue of users abstaining from show-related content during their watch. This is primarily because the definition of a spoiler varies depending on the viewer’s progress in the show, and conventional spoiler detection methods lack the granularity to capture this complexity. To tackle this challenge, we propose the task of spoiler matching, which involves assigning an episode number to a spoiler given a specific TV show. We frame this task as semantic text matching and introduce a dataset comprised of comments and episode summaries to evaluate model performance. Given the length of each example, our dataset can also serve as a benchmark for long-range language models.
%R 10.18653/v1/2023.emnlp-main.373
%U https://aclanthology.org/2023.emnlp-main.373
%U https://doi.org/10.18653/v1/2023.emnlp-main.373
%P 6109-6113
Markdown (Informal)
[Spoiler Detection as Semantic Text Matching](https://aclanthology.org/2023.emnlp-main.373) (Tran et al., EMNLP 2023)
ACL
- Ryan Tran, Canwen Xu, and Julian McAuley. 2023. Spoiler Detection as Semantic Text Matching. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6109–6113, Singapore. Association for Computational Linguistics.