@inproceedings{birkenheuer-etal-2023-sabrina,
    title = "Sabrina Spellman at {S}em{E}val-2023 Task 5: Discover the Shocking Truth Behind this Composite Approach to Clickbait Spoiling!",
    author = "Birkenheuer, Simon  and
      Drechsel, Jonathan  and
      Justen, Paul  and
      Phlmann, Jimmy  and
      Gonsior, Julius  and
      Reusch, Anja",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.134",
    doi = "10.18653/v1/2023.semeval-1.134",
    pages = "969--977",
    abstract = "This paper describes an approach to automat- ically close the knowledge gap of Clickbait- Posts via a transformer model trained for Question-Answering, augmented by a task- specific post-processing step. This was part of the SemEval 2023 Clickbait shared task (Frbe et al., 2023a) - specifically task 2. We devised strategies to improve the existing model to fit the task better, e.g. with different special mod- els 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 Learn- ing. With those adjustments, we were able to improve the baseline by 9.8 percentage points to a BLEU-4 score of 48.0{\%}.",
}

This paper describes an approach to automat- ically close the knowledge gap of Clickbait- Posts via a transformer model trained for Question-Answering, augmented by a task- specific post-processing step. This was part of the SemEval 2023 Clickbait shared task (Frbe et al., 2023a) - specifically task 2. We devised strategies to improve the existing model to fit the task better, e.g. with different special mod- els 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 Learn- ing. With those adjustments, we were able to improve the baseline by 9.8 percentage points to a BLEU-4 score of 48.0{\%}.
Birkenheuer, Simon Drechsel, Jonathan Justen, Paul Phlmann, Jimmy Gonsior, Julius Reusch, Anja
Sabrina Spellman at SemEval-2023 Task 5: Discover the Shocking Truth Behind this Composite Approach to Clickbait Spoiling!