# Transformer-Based Model for Multilingual Hope Speech Detection

arXiv: https://arxiv.org/abs/2602.00613 · HTML: https://mlsanity.com/paper/2602.00613/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Builds on (most cited first, 1 of 1)

- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)

## Abstract (arXiv)

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.