Spoiler in a Textstack: How Much Can Transformers Help?
This work addresses spoiler detection for users of review platforms, but it is incremental as it applies existing methods to a rarely studied problem.
The paper tackles spoiler detection in reviews by fine-tuning transformer architectures, achieving ROC AUC scores of 81% on the TV Tropes Movies dataset and 88% on the Goodreads dataset, and introduces a new dataset with fine-grained annotations.
This paper presents our research regarding spoiler detection in reviews. In this use case, we describe the method of fine-tuning and organizing the available text-based model tasks with the latest deep learning achievements and techniques to interpret the models' results. Until now, spoiler research has been rarely described in the literature. We tested the transfer learning approach and different latest transformer architectures on two open datasets with annotated spoilers (ROC AUC above 81\% on TV Tropes Movies dataset, and Goodreads dataset above 88\%). We also collected data and assembled a new dataset with fine-grained annotations. To that end, we employed interpretability techniques and measures to assess the models' reliability and explain their results.