Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning
Aditya Narayan Sankaran, Reza Farahbakhsh, Noel Crespi
Abstract
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.
BibTeX
@inproceedings{sankaran-etal-2025-towards,
title = "Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning",
author = "Sankaran, Aditya Narayan and
Farahbakhsh, Reza and
Crespi, Noel",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.373/",
pages = "5558--5569"
}