@inproceedings{pan-etal-2023-chick,
title = "Chick Adams at {S}em{E}val-2023 Task 5: Using {R}o{BERT}a and {D}e{BERT}a to Extract Post and Document-based Features for Clickbait Spoiling",
author = "Pan, Ronghao and
Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio and
Garc{\'\i}a-S{\'a}nchez, Franciso and
Valencia-Garc{\'\i}a, Rafael",
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-1.85",
doi = "10.18653/v1/2023.semeval-1.85",
pages = "624--628",
abstract = "In this manuscript, we describe the participation of the UMUTeam in SemEval-2023 Task 5, namely, Clickbait Spoiling, a shared task on identifying spoiler type (i.e., a phrase or a passage) and generating short texts that satisfy curiosity induced by a clickbait post, i.e. generating spoilers for the clickbait post. Our participation in Task 1 is based on fine-tuning pre-trained models, which consists in taking a pre-trained model and tuning it to fit the spoiler classification task. Our system has obtained excellent results in Task 1: we outperformed all proposed baselines, being within the Top 10 for most measures. Foremost, we reached Top 3 in F1 score in the passage spoiler ranking.",
}
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<abstract>In this manuscript, we describe the participation of the UMUTeam in SemEval-2023 Task 5, namely, Clickbait Spoiling, a shared task on identifying spoiler type (i.e., a phrase or a passage) and generating short texts that satisfy curiosity induced by a clickbait post, i.e. generating spoilers for the clickbait post. Our participation in Task 1 is based on fine-tuning pre-trained models, which consists in taking a pre-trained model and tuning it to fit the spoiler classification task. Our system has obtained excellent results in Task 1: we outperformed all proposed baselines, being within the Top 10 for most measures. Foremost, we reached Top 3 in F1 score in the passage spoiler ranking.</abstract>
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%0 Conference Proceedings
%T Chick Adams at SemEval-2023 Task 5: Using RoBERTa and DeBERTa to Extract Post and Document-based Features for Clickbait Spoiling
%A Pan, Ronghao
%A García-Díaz, José Antonio
%A García-Sánchez, Franciso
%A Valencia-García, Rafael
%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 pan-etal-2023-chick
%X In this manuscript, we describe the participation of the UMUTeam in SemEval-2023 Task 5, namely, Clickbait Spoiling, a shared task on identifying spoiler type (i.e., a phrase or a passage) and generating short texts that satisfy curiosity induced by a clickbait post, i.e. generating spoilers for the clickbait post. Our participation in Task 1 is based on fine-tuning pre-trained models, which consists in taking a pre-trained model and tuning it to fit the spoiler classification task. Our system has obtained excellent results in Task 1: we outperformed all proposed baselines, being within the Top 10 for most measures. Foremost, we reached Top 3 in F1 score in the passage spoiler ranking.
%R 10.18653/v1/2023.semeval-1.85
%U https://aclanthology.org/2023.semeval-1.85
%U https://doi.org/10.18653/v1/2023.semeval-1.85
%P 624-628
Markdown (Informal)
[Chick Adams at SemEval-2023 Task 5: Using RoBERTa and DeBERTa to Extract Post and Document-based Features for Clickbait Spoiling](https://aclanthology.org/2023.semeval-1.85) (Pan et al., SemEval 2023)
ACL