Transformer-Based Model for Multilingual Hope Speech Detection

• URL: http://arxiv.org/abs/2602.00613v1
• Date: Sat, 31 Jan 2026 09:06:01 GMT
• Title: Transformer-Based Model for Multilingual Hope Speech Detection
• Abstract summary: RoBERTa has been implemented for English, while the multilingual model XLM-RoBERTa has been implemented for both English and German languages.<n>The proposed system using RoBERTa reported a weighted f1-score of 0.818 and an accuracy of 81.8% for English.<n>On the other hand, XLM-RoBERTa achieved a weighted f1-score of 0.786 and an accuracy of 78.5%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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