MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector

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 firs...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Costa-jussà, Marta R, Mariano Coria Meglioli, Andrews, Pierre, Dale, David, Hansanti, Prangthip, Kalbassi, Elahe, Mourachko, Alex, Ropers, Christophe, Wood, Carleigh
Format: Article
Language:English
Subjects:
Classifiers
Datasets
English language
Natural language processing
Non-English languages
Online Access:Get full text
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Summary: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. The dataset comprises 20,000 audio utterances for English and Spanish, and 4,000 for the other 19 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 outperforms existing text-based trainable classifiers by more than 1% AUC, while expanding the language coverage more than tenfold. When compared to a wordlist-based classifier that covers a similar number of languages, MuTox improves precision and recall by approximately 2.5 times. This significant improvement underscores the potential of MuTox in advancing the field of audio-based toxicity detection.
ISSN:2331-8422