朝向在資源稀缺環境中進行跨語言音訊濫用偵測的研究：少樣本學習

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

December 2, 2024

作者: Aditya Narayan Sankaran, Reza Farahbaksh, Noel Crespi

cs.AI

摘要

在線濫用內容檢測，特別是在資源有限的情況下以及在音頻模式下的檢測，仍然是一個未被充分探索的領域。我們研究了預訓練音頻表示對於在資源有限的語言中檢測濫用語言的潛力，具體來說，在印度語言中使用少樣本學習（FSL）。利用來自Wav2Vec和Whisper等模型的強大表示，我們探索了使用ADIMA數據集和FSL進行跨語言濫用檢測。我們的方法將這些表示集成到模型不可知元學習（MAML）框架中，以對10種語言中的濫用語言進行分類。我們通過評估有限數據對性能的影響，實驗了各種樣本大小（50-200）。此外，還進行了特徵可視化研究，以更好地理解模型行為。這項研究突出了預訓練模型在資源有限情況下的泛化能力，並提供了有價值的見解，可用於在多語境中檢測濫用語言。

English

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.