Transformer-Based Model for Multilingual Hope Speech Detection

Nsrin Ashraf, Mariam Labib, Hamada Nayel
1/31/2026
cs.CL

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Abstract

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.

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Cite this paper

@article{ashraf2026transformerbased,
  title  = {Transformer-Based Model for Multilingual Hope Speech Detection},
  author = {Nsrin Ashraf and Mariam Labib and Hamada Nayel},
  year   = {2026},
  eprint = {2602.00613},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.00613v1}
}

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