GHaLIB: A Multilingual Framework for Hope Speech Detection in Low-Resource Languages

2025-12-27

AI Summary

This study addresses the scarcity of hope speech detection research for low-resource languages—particularly Urdu—and the limited cross-lingual generalization of existing models. We propose the first lightweight, multilingual adaptation framework for hope speech recognition. Methodologically, we systematically evaluate the cross-lingual transfer performance of pretrained multilingual models—including XLM-RoBERTa, mBERT, EuroBERT, and UrduBERT—on hope speech detection, employing simple text preprocessing and supervised fine-tuning for efficient binary/multiclass classification. On the PolyHope-M 2025 benchmark, our approach achieves 95.2% F1 for Urdu binary classification and 65.2% F1 for multiclass classification, with robust performance also observed for Spanish, German, and English. Our key contributions are threefold: (1) filling critical resource and methodological gaps in hope speech detection for low-resource languages; (2) providing the first empirical validation of multilingual Transformers’ generalization capability for positive discourse detection; and (3) introducing an extensible, lightweight adaptation paradigm.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Hope speech has been relatively underrepresented in Natural Language Processing (NLP). Current studies are largely focused on English, which has resulted in a lack of resources for low-resource languages such as Urdu. As a result, the creation of tools that facilitate positive online communication remains limited. Although transformer-based architectures have proven to be effective in detecting hate and offensive speech, little has been done to apply them to hope speech or, more generally, to test them across a variety of linguistic settings. This paper presents a multilingual framework for hope speech detection with a focus on Urdu. Using pretrained transformer models such as XLM-RoBERTa, mBERT, EuroBERT, and UrduBERT, we apply simple preprocessing and train classifiers for improved results. 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. These results highlight the possibility of implementing existing multilingual models in low-resource environments, thus making it easier to identify hope speech and helping to build a more constructive digital discourse.

Problem

• Detect hope speech in low-resource languages like Urdu
• Apply transformer models to hope speech across linguistic settings
• Build tools for positive online communication in multilingual contexts

Innovation

• Multilingual framework using pretrained transformer models
• Focus on low-resource languages like Urdu for hope speech detection
• Achieves high F1-scores in binary and multi-class classification