This paper describes a system that has been submitted to the "PolyHope-M" at RANLP2025. In this work various transformers have been implemented and evaluated for hope speech detection for English and Germany. RoBERTa has been implemented for English, while the multilingual model XLM-RoBERTa has been implemented for both English and German languages. The proposed system using RoBERTa reported a weighted f1-score of 0.818 and an accuracy of 81.8% for English. On the other hand, XLM-RoBERTa achieved a weighted f1-score of 0.786 and an accuracy of 78.5%. These results reflects the importance of improvement of pre-trained large language models and how these models enhancing the performance of different natural language processing tasks.
@article{arxiv.2602.00613,
title = {Transformer-Based Model for Multilingual Hope Speech Detection},
author = {Nsrin Ashraf and Mariam Labib and Hamada Nayel},
journal= {arXiv preprint arXiv:2602.00613},
year = {2026}
}
Comments
5 pages, 1 figure, PolyHope-M shared task at RANLP2025