@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Diane Simmons at SemEval-2023 Task 5: Is it possible to make good clickbait spoilers using a Zero-Shot approach? Check it out!
%A Krog, Niels
%A Agirrezabal, Manex
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F krog-agirrezabal-2023-diane
%X 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.
%R 10.18653/v1/2023.semeval-1.66
%U https://aclanthology.org/2023.semeval-1.66
%U https://doi.org/10.18653/v1/2023.semeval-1.66
%P 477-481
Markdown (Informal)
[Diane Simmons at SemEval-2023 Task 5: Is it possible to make good clickbait spoilers using a Zero-Shot approach? Check it out!](https://aclanthology.org/2023.semeval-1.66) (Krog & Agirrezabal, SemEval 2023)
ACL