title: Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings
    with Few-Shot Learning
summary: 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.
paper_authors._id: 674ef0ea044f82a94169d30d
paper_authors.name: Aditya Narayan Sankaran
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-03T12:00:13.468Z
paper_authors._id: 62d923892010d671bc9e67cc
paper_authors.avatarUrl: /avatars/6b3d7c741755615f337aa909d1a53d55.svg
paper_authors.fullname: Aditya Narayan Sankaran
paper_authors.type: user
paper_authors.user: callmesan
paper_authors._id: 674ef0ea044f82a94169d30e
paper_authors.name: Reza Farahbaksh
paper_authors._id: 674ef0ea044f82a94169d30f
paper_authors.name: Noel Crespi
paper_title: Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings
    with Few-Shot Learning
paper_summary: 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.
paper_ai_summary: Investigating pre-trained audio representations like Wav2Vec and Whisper for abusive language detection in low-resource languages using Few Shot Learning within the MAML framework.
paper_ai_keywords: pre-trained audio representations Few Shot Learning (FSL) Wav2Vec Whisper ADIMA dataset Model-Agnostic Meta-Learning (MAML) feature visualization low-resource languages cross-lingual abuse detection