@inproceedings{vasconcelos-etal-2024-bambas,
title = "{BAMBAS} at {S}em{E}val-2024 Task 4: How far can we get without looking at hierarchies?",
author = "Vasconcelos, Arthur and
De Melo, Luiz Felipe and
Goncalves, Eduardo and
Bezerra, Eduardo and
Paes, Aline and
Plastino, Alexandre",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.70/",
doi = "10.18653/v1/2024.semeval-1.70",
pages = "455--462",
abstract = "This paper describes the BAMBAS team`s participation in SemEval-2024 Task 4 Subtask 1, which focused on the multilabel classification of persuasion techniques in the textual content of Internet memes. We explored a lightweight approach that does not consider the hierarchy of labels. First, we get the text embeddings leveraging the multilingual tweets-based language model, Bernice. Next, we use those embeddings to train a separate binary classifier for each label, adopting independent oversampling strategies in each model in a binary-relevance style. We tested our approach over the English dataset, exceeding the baseline by 21 percentage points, while ranking in 23th in terms of hierarchical F1 and 11st in terms of hierarchical recall."
}
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<abstract>This paper describes the BAMBAS team‘s participation in SemEval-2024 Task 4 Subtask 1, which focused on the multilabel classification of persuasion techniques in the textual content of Internet memes. We explored a lightweight approach that does not consider the hierarchy of labels. First, we get the text embeddings leveraging the multilingual tweets-based language model, Bernice. Next, we use those embeddings to train a separate binary classifier for each label, adopting independent oversampling strategies in each model in a binary-relevance style. We tested our approach over the English dataset, exceeding the baseline by 21 percentage points, while ranking in 23th in terms of hierarchical F1 and 11st in terms of hierarchical recall.</abstract>
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%0 Conference Proceedings
%T BAMBAS at SemEval-2024 Task 4: How far can we get without looking at hierarchies?
%A Vasconcelos, Arthur
%A De Melo, Luiz Felipe
%A Goncalves, Eduardo
%A Bezerra, Eduardo
%A Paes, Aline
%A Plastino, Alexandre
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F vasconcelos-etal-2024-bambas
%X This paper describes the BAMBAS team‘s participation in SemEval-2024 Task 4 Subtask 1, which focused on the multilabel classification of persuasion techniques in the textual content of Internet memes. We explored a lightweight approach that does not consider the hierarchy of labels. First, we get the text embeddings leveraging the multilingual tweets-based language model, Bernice. Next, we use those embeddings to train a separate binary classifier for each label, adopting independent oversampling strategies in each model in a binary-relevance style. We tested our approach over the English dataset, exceeding the baseline by 21 percentage points, while ranking in 23th in terms of hierarchical F1 and 11st in terms of hierarchical recall.
%R 10.18653/v1/2024.semeval-1.70
%U https://aclanthology.org/2024.semeval-1.70/
%U https://doi.org/10.18653/v1/2024.semeval-1.70
%P 455-462
Markdown (Informal)
[BAMBAS at SemEval-2024 Task 4: How far can we get without looking at hierarchies?](https://aclanthology.org/2024.semeval-1.70/) (Vasconcelos et al., SemEval 2024)
ACL