Spoiler Detection as Semantic Text Matching
Submission Type: Regular Short Paper
Submission Track: NLP Applications
Submission Track 2: Resources and Evaluation
Keywords: dataset, spoiler detection, semantic text matching
TL;DR: We propose a new task that formulates spoiler detection as semantic text matching.
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.
Submission Number: 325
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