Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings
  with Few-Shot Learning
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.
674ef0ea044f82a94169d30d
Aditya Narayan Sankaran
claimed_verified
2024-12-03T12:00:13.468Z
62d923892010d671bc9e67cc
/avatars/6b3d7c741755615f337aa909d1a53d55.svg
Aditya Narayan Sankaran
user
callmesan
674ef0ea044f82a94169d30e
Reza Farahbaksh
674ef0ea044f82a94169d30f
Noel Crespi
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings
  with Few-Shot Learning
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.
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.
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