@inproceedings{villa-cueva-etal-2023-walter,
title = "Walter Burns at {S}em{E}val-2023 Task 5: {NLP}-{CIMAT} - Leveraging Model Ensembles for Clickbait Spoiling",
author = "Villa Cueva, Emilio and
Vallejo Aldana, Daniel and
S{\'a}nchez Vega, Fernando and
L{\'o}pez Monroy, Adri{\'a}n Pastor",
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-1.95/",
doi = "10.18653/v1/2023.semeval-1.95",
pages = "693--699",
abstract = "This paper describes our participation in the Clickbait challenge at SemEval 2023. In this work, we address the Clickbait classification task using transformers models in an ensemble configuration. We tackle the Spoiler Generation task using a two-level ensemble strategy of models trained for extractive QA, and selecting the best K candidates for multi-part spoilers. In the test partitions, our approaches obtained a classification accuracy of 0.716 for classification and a BLEU-4 score of 0.439 for spoiler generation."
}
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%0 Conference Proceedings
%T Walter Burns at SemEval-2023 Task 5: NLP-CIMAT - Leveraging Model Ensembles for Clickbait Spoiling
%A Villa Cueva, Emilio
%A Vallejo Aldana, Daniel
%A Sánchez Vega, Fernando
%A López Monroy, Adrián Pastor
%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 villa-cueva-etal-2023-walter
%X This paper describes our participation in the Clickbait challenge at SemEval 2023. In this work, we address the Clickbait classification task using transformers models in an ensemble configuration. We tackle the Spoiler Generation task using a two-level ensemble strategy of models trained for extractive QA, and selecting the best K candidates for multi-part spoilers. In the test partitions, our approaches obtained a classification accuracy of 0.716 for classification and a BLEU-4 score of 0.439 for spoiler generation.
%R 10.18653/v1/2023.semeval-1.95
%U https://aclanthology.org/2023.semeval-1.95/
%U https://doi.org/10.18653/v1/2023.semeval-1.95
%P 693-699
Markdown (Informal)
[Walter Burns at SemEval-2023 Task 5: NLP-CIMAT - Leveraging Model Ensembles for Clickbait Spoiling](https://aclanthology.org/2023.semeval-1.95/) (Villa Cueva et al., SemEval 2023)
ACL