Multilingual Hope Speech Detection Framework for Low-Resource Languages
Paper#NLP, Hope Speech Detection, Multilingual, Low-Resource Languages, Transformers🔬 Research|Analyzed: Jan 3, 2026 16:22•
Published: Dec 27, 2025 21:23
•1 min read
•ArXivAnalysis
This paper addresses the under-representation of hope speech in NLP, particularly in low-resource languages like Urdu. It leverages pre-trained transformer models (XLM-RoBERTa, mBERT, EuroBERT, UrduBERT) to create a multilingual framework for hope speech detection. The focus on Urdu and the strong performance on the PolyHope-M 2025 benchmark, along with competitive results in other languages, demonstrates the potential of applying existing multilingual models in resource-constrained environments to foster positive online communication.
Key Takeaways
- •Proposes a multilingual framework for hope speech detection.
- •Focuses on low-resource languages, particularly Urdu.
- •Utilizes pre-trained transformer models (XLM-RoBERTa, mBERT, etc.).
- •Achieves strong performance on the PolyHope-M 2025 benchmark.
- •Demonstrates the feasibility of applying multilingual models in resource-constrained settings.
Reference / Citation
View Original"Evaluations on the PolyHope-M 2025 benchmark demonstrate strong performance, achieving F1-scores of 95.2% for Urdu binary classification and 65.2% for Urdu multi-class classification, with similarly competitive results in Spanish, German, and English."