Sabrina Spellman at SemEval-2023 Task 5: Discover the Shocking Truth Behind this Composite Approach to Clickbait Spoiling!
Simon Birkenheuer, Jonathan Drechsel, P.C. Justen, Jimmy Phlmann, Julius Gonsior, Anja Reusch · 2023
This paper describes an approach to automatically close the knowledge gap of Clickbait-Posts via a transformer model trained for Question-Answering, augmented by a taskspecific post-processing step.This was part of the SemEval 2023 Clickbait shared task (Fröbe et al., 2023a) -specifically task 5. We devised strategies to improve the existing model to fit the task better, e.g. with different special models and a post-processor tailored to different inherent challenges of the task.Furthermore, we explored the possibility of expanding the original training data by using strategies from Heuristic Labeling and Semi-Supervised Learning.With those adjustments, we were able to improve the baseline by 9.8 percentage points to a BLEU-4 score of 48.0%.