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arXiv · 2401.05060

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

Marta R. Costa-jussà·Mariano Coria Meglioli·Pierre Andrews·David Dale·Prangthip Hansanti·Elahe Kalbassi·Alex Mourachko·Christophe Ropers·Carleigh Wood
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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. 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.

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Marta R. Costa-jussà, Mariano Coria Meglioli, Pierre Andrews, David Dale, Prangthip Hansanti, Elahe Kalbassi, Alex Mourachko, Christophe Ropers, Carleigh Wood. 2024-01-10. MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector. https://arxiv.org/abs/2401.05060

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