Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning
This work addresses the problem of detecting encouraging online speech in multilingual and low-resource settings, but it is incremental as it applies existing methods to a specific domain.
The paper tackled multilingual hope speech detection by comparing logistic regression, mBERT, and XLM-RoBERTa with active learning, finding that transformer models significantly outperformed baselines, with XLM-RoBERTa achieving the highest accuracy and active learning maintaining strong performance with small datasets.
Hope speech language that fosters encouragement and optimism plays a vital role in promoting positive discourse online. However, its detection remains challenging, especially in multilingual and low-resource settings. This paper presents a multilingual framework for hope speech detection using an active learning approach and transformer-based models, including mBERT and XLM-RoBERTa. Experiments were conducted on datasets in English, Spanish, German, and Urdu, including benchmark test sets from recent shared tasks. Our results show that transformer models significantly outperform traditional baselines, with XLM-RoBERTa achieving the highest overall accuracy. Furthermore, our active learning strategy maintained strong performance even with small annotated datasets. This study highlights the effectiveness of combining multilingual transformers with data-efficient training strategies for hope speech detection.