GHaLIB: A Multilingual Framework for Hope Speech Detection in Low-Resource Languages
This work addresses the lack of resources for hope speech detection in low-resource languages, enabling more positive online communication, though it is incremental as it applies existing models to new data.
The paper tackled the problem of hope speech detection in low-resource languages like Urdu by developing a multilingual framework using pretrained transformer models, achieving F1-scores of 95.2% for binary classification and 65.2% for multi-class classification in Urdu.
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.