@inproceedings{pauli-etal-2023-teamampa,
title = "{T}eam{A}mpa at {S}em{E}val-2023 Task 3: Exploring Multilabel and Multilingual {R}o{BERT}a Models for Persuasion and Framing Detection",
author = "Pauli, Amalie and
Sarabia, Rafael and
Derczynski, Leon and
Assent, Ira",
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-1.117",
doi = "10.18653/v1/2023.semeval-1.117",
pages = "847--855",
abstract = "This paper describes our submission to theSemEval 2023 Task 3 on two subtasks: detectingpersuasion techniques and framing. Bothsubtasks are multi-label classification problems. We present a set of experiments, exploring howto get robust performance across languages usingpre-trained RoBERTa models. We test differentoversampling strategies, a strategy ofadding textual features from predictions obtainedwith related models, and present bothinconclusive and negative results. We achievea robust ranking across languages and subtaskswith our best ranking being nr. 1 for Subtask 3on Spanish.",
}
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<abstract>This paper describes our submission to theSemEval 2023 Task 3 on two subtasks: detectingpersuasion techniques and framing. Bothsubtasks are multi-label classification problems. We present a set of experiments, exploring howto get robust performance across languages usingpre-trained RoBERTa models. We test differentoversampling strategies, a strategy ofadding textual features from predictions obtainedwith related models, and present bothinconclusive and negative results. We achievea robust ranking across languages and subtaskswith our best ranking being nr. 1 for Subtask 3on Spanish.</abstract>
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%0 Conference Proceedings
%T TeamAmpa at SemEval-2023 Task 3: Exploring Multilabel and Multilingual RoBERTa Models for Persuasion and Framing Detection
%A Pauli, Amalie
%A Sarabia, Rafael
%A Derczynski, Leon
%A Assent, Ira
%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 pauli-etal-2023-teamampa
%X This paper describes our submission to theSemEval 2023 Task 3 on two subtasks: detectingpersuasion techniques and framing. Bothsubtasks are multi-label classification problems. We present a set of experiments, exploring howto get robust performance across languages usingpre-trained RoBERTa models. We test differentoversampling strategies, a strategy ofadding textual features from predictions obtainedwith related models, and present bothinconclusive and negative results. We achievea robust ranking across languages and subtaskswith our best ranking being nr. 1 for Subtask 3on Spanish.
%R 10.18653/v1/2023.semeval-1.117
%U https://aclanthology.org/2023.semeval-1.117
%U https://doi.org/10.18653/v1/2023.semeval-1.117
%P 847-855
Markdown (Informal)
[TeamAmpa at SemEval-2023 Task 3: Exploring Multilabel and Multilingual RoBERTa Models for Persuasion and Framing Detection](https://aclanthology.org/2023.semeval-1.117) (Pauli et al., SemEval 2023)
ACL