用Transformer检测多语言希望言论，英德语表现良好。
Transformer-Based Model for Multilingual Hope Speech Detection
- 采用RoBERTa和XLM-RoBERTa分别处理英语和德语希望言论。
- 英语任务达81.8%准确率，加权F1为0.818；德语为78.5%准确率，加权F1为0.786。
- 展示预训练模型对多语言情感识别的有效性，适合关注跨语言NLP的读者。
本文介绍了提交至RANLP2025'PolyHope-M'的系统。针对英语和德语的希望言论检测任务，分别使用RoBERTa和多语言模型XLM-RoBERTa进行实现与评估。基于RoBERTa的系统在英语任务中取得81.8%的准确率与0.818的加权F1分数；而XLM-RoBERTa在英德双语任务中分别获得78.5%的准确率和0.786的加权F1分数。结果表明，预训练大语言模型的优化对提升自然语言处理任务性能具有重要意义。
原文摘要 · Abstract (English)
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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