@inproceedings{krog-agirrezabal-2023-diane,
    title = "Diane Simmons at {S}em{E}val-2023 Task 5: Is it possible to make good clickbait spoilers using a Zero-Shot approach? Check it out!",
    author = "Krog, Niels  and
      Agirrezabal, Manex",
    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-1.66",
    doi = "10.18653/v1/2023.semeval-1.66",
    pages = "477--481",
    abstract = "In this paper, we present a possible solution to the SemEval23 shared task of generating spoilers for clickbait headlines. Using a Zero-Shot approach with two different Transformer architectures, BLOOM and RoBERTa, we generate three different types of spoilers: phrase, passage and multi. We found, RoBERTa pretrained for Question-Answering to perform better than BLOOM for causal language modelling, however both architectures proved promising for future attempts at such tasks.",
}

In this paper, we present a possible solution to the SemEval23 shared task of generating spoilers for clickbait headlines. Using a Zero-Shot approach with two different Transformer architectures, BLOOM and RoBERTa, we generate three different types of spoilers: phrase, passage and multi. We found, RoBERTa pretrained for Question-Answering to perform better than BLOOM for causal language modelling, however both architectures proved promising for future attempts at such tasks.
Krog, Niels Agirrezabal, Manex
Diane Simmons at SemEval-2023 Task 5: Is it possible to make good clickbait spoilers using a Zero-Shot approach? Check it out!