@inproceedings{vitiugin-paakki-2024-ensemble,
title = "Ensemble-based Multilingual Euphemism Detection: a Behavior-Guided Approach",
author = "Vitiugin, Fedor and
Paakki, Henna",
editor = "Ghosh, Debanjan and
Muresan, Smaranda and
Feldman, Anna and
Chakrabarty, Tuhin and
Liu, Emmy",
booktitle = "Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.figlang-1.10",
doi = "10.18653/v1/2024.figlang-1.10",
pages = "73--78",
abstract = "This paper describes the system submitted by our team to the Multilingual Euphemism Detection Shared Task for the Fourth Workshop on Figurative Language Processing (FigLang 2024). We propose a novel model for multilingual euphemism detection, combining contextual and behavior-related features. The system classifies texts that potentially contain euphemistic terms with an ensemble classifier based on outputs from behavior-related fine-tuned models. Our results show that, for this kind of task, our model outperforms baselines and state-of-the-art euphemism detection methods. As for the leader-board, our classification model achieved a macro averaged F1 score of [anonymized], reaching the [anonymized] place.",
}
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<abstract>This paper describes the system submitted by our team to the Multilingual Euphemism Detection Shared Task for the Fourth Workshop on Figurative Language Processing (FigLang 2024). We propose a novel model for multilingual euphemism detection, combining contextual and behavior-related features. The system classifies texts that potentially contain euphemistic terms with an ensemble classifier based on outputs from behavior-related fine-tuned models. Our results show that, for this kind of task, our model outperforms baselines and state-of-the-art euphemism detection methods. As for the leader-board, our classification model achieved a macro averaged F1 score of [anonymized], reaching the [anonymized] place.</abstract>
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%0 Conference Proceedings
%T Ensemble-based Multilingual Euphemism Detection: a Behavior-Guided Approach
%A Vitiugin, Fedor
%A Paakki, Henna
%Y Ghosh, Debanjan
%Y Muresan, Smaranda
%Y Feldman, Anna
%Y Chakrabarty, Tuhin
%Y Liu, Emmy
%S Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico (Hybrid)
%F vitiugin-paakki-2024-ensemble
%X This paper describes the system submitted by our team to the Multilingual Euphemism Detection Shared Task for the Fourth Workshop on Figurative Language Processing (FigLang 2024). We propose a novel model for multilingual euphemism detection, combining contextual and behavior-related features. The system classifies texts that potentially contain euphemistic terms with an ensemble classifier based on outputs from behavior-related fine-tuned models. Our results show that, for this kind of task, our model outperforms baselines and state-of-the-art euphemism detection methods. As for the leader-board, our classification model achieved a macro averaged F1 score of [anonymized], reaching the [anonymized] place.
%R 10.18653/v1/2024.figlang-1.10
%U https://aclanthology.org/2024.figlang-1.10
%U https://doi.org/10.18653/v1/2024.figlang-1.10
%P 73-78
Markdown (Informal)
[Ensemble-based Multilingual Euphemism Detection: a Behavior-Guided Approach](https://aclanthology.org/2024.figlang-1.10) (Vitiugin & Paakki, Fig-Lang-WS 2024)
ACL