Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning

This is the code repository for our paper published as part of the proceedings of COLING 2025.

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.

Overview of the codebase

Folders

1. data: Contains the script to download the dataset with the extracted embeddings.
2. feature-extraction: Contains scripts to extract features from audio files of the ADIMA dataset.
3. plots: Contains plots of Accuracy Scores and tSNE experiments.
4. results: Contains csv files with FSL experiment results.
5. utils: Contains code for the FSL experiments.

Code files

1. fsl-whisper.py: To run the Whisper-Related Experiments
2. fsl-wav2vec.py: To run the Wav2Vec-Related Experiments
3. results-plot.ipynb: Contains code to the plots presented in the paper.

Refer to the Original Authors Repository ADIMA for the audio files and annotations.

Requirements

We performed the experiments with Python 3.11. The requriments.txt contains the necessary libraries needed for the task.

Citation

Please use the following citation in case you use our work:

@inproceedings{sankaran2025towards,
    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",
    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."
}