Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912154287865856 |
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| author | Sankaran, Aditya Narayan Farahbakhsh, Reza Crespi, Noel |
| author_facet | Sankaran, Aditya Narayan Farahbakhsh, Reza Crespi, Noel |
| contents | Online abusive content detection, particularly in low-resource settings and within the audio modality, remains underexplored. We investigate the potential of pre-trained audio representations for detecting abusive language in low-resource languages, in this case, in Indian languages using Few Shot Learning (FSL). Leveraging powerful representations from models such as Wav2Vec and Whisper, we explore cross-lingual abuse detection using the ADIMA dataset with FSL. Our approach integrates these representations within the Model-Agnostic Meta-Learning (MAML) framework to classify abusive language in 10 languages. We experiment with various shot sizes (50-200) evaluating the impact of limited data on performance. Additionally, a feature visualization study was conducted to better understand model behaviour. This study highlights the generalization ability of pre-trained models in low-resource scenarios and offers valuable insights into detecting abusive language in multilingual contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01408 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning Sankaran, Aditya Narayan Farahbakhsh, Reza Crespi, Noel Computation and Language Artificial Intelligence Online abusive content detection, particularly in low-resource settings and within the audio modality, remains underexplored. We investigate the potential of pre-trained audio representations for detecting abusive language in low-resource languages, in this case, in Indian languages using Few Shot Learning (FSL). Leveraging powerful representations from models such as Wav2Vec and Whisper, we explore cross-lingual abuse detection using the ADIMA dataset with FSL. Our approach integrates these representations within the Model-Agnostic Meta-Learning (MAML) framework to classify abusive language in 10 languages. We experiment with various shot sizes (50-200) evaluating the impact of limited data on performance. Additionally, a feature visualization study was conducted to better understand model behaviour. This study highlights the generalization ability of pre-trained models in low-resource scenarios and offers valuable insights into detecting abusive language in multilingual contexts. |
| title | Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.01408 |