多语言希望言语检测：逻辑回归、mBERT 与 XLM-RoBERTa 结合主动学习的比较研究
Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning
AI总结：
本研究提出结合主动学习与多语言变换器（mBERT、XLM-RoBERTa）的希望言语检测框架，在四种语言上验证其优于传统基线，并实现数据高效训练。
摘要：
希望言语是一种鼓励和乐观的语言，在促进线上积极话语方面发挥着至关重要的作用。然而，其检测仍然具有挑战性，尤其是在多语言和低资源环境中。本文提出了一种多语言希望言语检测框架，采用主动学习方法并基于变换器模型，包括 mBERT 和 XLM-RoBERTa。实验在英语、西班牙语、德语和乌尔都语的数据集上进行，包括来自近期共享任务的基准测试集。我们的结果表明，变换器模型显著优于传统基线，其中 XLM-RoBERTa 取得了最高的总体准确率。此外，我们的主动学习策略即使在小规模标注数据集上也保持了强劲的性能。本研究强调了将多语言变换器与数据高效训练策略相结合对希望言语检测的有效性。
英文摘要：
Hope speech language that fosters encouragement and optimism plays a vital role in promoting positive discourse online. However, its detection remains challenging, especially in multilingual and low-resource settings. This paper presents a multilingual framework for hope speech detection using an active learning approach and transformer-based models, including mBERT and XLM-RoBERTa. Experiments were conducted on datasets in English, Spanish, German, and Urdu, including benchmark test sets from recent shared tasks. Our results show that transformer models significantly outperform traditional baselines, with XLM-RoBERTa achieving the highest overall accuracy. Furthermore, our active learning strategy maintained strong performance even with small annotated datasets. This study highlights the effectiveness of combining multilingual transformers with data-efficient training strategies for hope speech detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
