@inproceedings{costa-jussa-etal-2024-mutox,
title = "{M}u{T}ox: Universal {MU}ltilingual Audio-based {TOX}icity Dataset and Zero-shot Detector",
author = "Costa-juss{\`a}, Marta and
Meglioli, Mariano and
Andrews, Pierre and
Dale, David and
Hansanti, Prangthip and
Kalbassi, Elahe and
Mourachko, Alexandre and
Ropers, Christophe and
Wood, Carleigh",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.340",
doi = "10.18653/v1/2024.findings-acl.340",
pages = "5725--5734",
abstract = "Research in toxicity detection in natural language processing for the speech modality (audio-based) is quite limited, particularly for languages other than English. To address these limitations and lay the groundwork for truly multilingual audio-based toxicity detection, we introduce MuTox, the first highly multilingual audio-based dataset with toxicity labels which covers 14 different linguistic families. The dataset comprises 20,000 audio utterances for English and Spanish, and 4,000 for the other 28 languages. To demonstrate the quality of this dataset, we trained the MuTox audio-based toxicity classifier, which enables zero-shot toxicity detection across a wide range of languages. This classifier performs on par with existing text-based trainable classifiers, while expanding the language coverage more than tenfold. When compared to a wordlist-based classifier that covers a similar number of languages, MuTox improves F1-Score by an average of 100{\%}. This significant improvement underscores the potential of MuTox in advancing the field of audio-based toxicity detection.",
}
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<abstract>Research in toxicity detection in natural language processing for the speech modality (audio-based) is quite limited, particularly for languages other than English. To address these limitations and lay the groundwork for truly multilingual audio-based toxicity detection, we introduce MuTox, the first highly multilingual audio-based dataset with toxicity labels which covers 14 different linguistic families. The dataset comprises 20,000 audio utterances for English and Spanish, and 4,000 for the other 28 languages. To demonstrate the quality of this dataset, we trained the MuTox audio-based toxicity classifier, which enables zero-shot toxicity detection across a wide range of languages. This classifier performs on par with existing text-based trainable classifiers, while expanding the language coverage more than tenfold. When compared to a wordlist-based classifier that covers a similar number of languages, MuTox improves F1-Score by an average of 100%. This significant improvement underscores the potential of MuTox in advancing the field of audio-based toxicity detection.</abstract>
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%0 Conference Proceedings
%T MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector
%A Costa-jussà, Marta
%A Meglioli, Mariano
%A Andrews, Pierre
%A Dale, David
%A Hansanti, Prangthip
%A Kalbassi, Elahe
%A Mourachko, Alexandre
%A Ropers, Christophe
%A Wood, Carleigh
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F costa-jussa-etal-2024-mutox
%X Research in toxicity detection in natural language processing for the speech modality (audio-based) is quite limited, particularly for languages other than English. To address these limitations and lay the groundwork for truly multilingual audio-based toxicity detection, we introduce MuTox, the first highly multilingual audio-based dataset with toxicity labels which covers 14 different linguistic families. The dataset comprises 20,000 audio utterances for English and Spanish, and 4,000 for the other 28 languages. To demonstrate the quality of this dataset, we trained the MuTox audio-based toxicity classifier, which enables zero-shot toxicity detection across a wide range of languages. This classifier performs on par with existing text-based trainable classifiers, while expanding the language coverage more than tenfold. When compared to a wordlist-based classifier that covers a similar number of languages, MuTox improves F1-Score by an average of 100%. This significant improvement underscores the potential of MuTox in advancing the field of audio-based toxicity detection.
%R 10.18653/v1/2024.findings-acl.340
%U https://aclanthology.org/2024.findings-acl.340
%U https://doi.org/10.18653/v1/2024.findings-acl.340
%P 5725-5734
Markdown (Informal)
[MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector](https://aclanthology.org/2024.findings-acl.340) (Costa-jussà et al., Findings 2024)
ACL
- Marta Costa-jussà, Mariano Meglioli, Pierre Andrews, David Dale, Prangthip Hansanti, Elahe Kalbassi, Alexandre Mourachko, Christophe Ropers, and Carleigh Wood. 2024. MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5725–5734, Bangkok, Thailand. Association for Computational Linguistics.