具有不确定性意识的半监督集成教师框架用于多语言抑郁症检测
Uncertainty-aware Semi-supervised Ensemble Teacher Framework for Multilingual Depression Detection
AI总结：
本文提出Semi-SMDNet框架，通过半监督学习、集成学习和数据增强，提升多语言抑郁症检测的鲁棒性和跨语言性能。
摘要：
从社交媒体文本中检测抑郁症仍然是一项具有挑战性的任务。这主要是由于不同的语言风格、非正式表达以及许多语言中标注数据的缺乏。为了解决这些问题，我们提出了Semi-SMDNet，一个强大的半监督多语言抑郁症检测网络。它结合了教师-学生伪标签、集成学习和数据增强。我们的框架使用一组教师模型。它们的预测通过软投票进行整合。基于不确定性的阈值过滤掉低置信度的伪标签以减少噪声并提高学习稳定性。我们还使用了置信度加权训练方法，专注于可靠的伪标签样本。这大大提高了跨语言的鲁棒性。在阿拉伯语、孟加拉语、英语和西班牙语数据集上的测试表明，我们的方法在各种情况下均优于强大的基线。它显著缩小了资源充足与资源不足设置之间的性能差距。详细的实验和研究证实了我们的框架的有效性，并且可以用于各种情况。这表明它适用于标注资源有限的可扩展、跨语言心理健康监测。
英文摘要：
Detecting depression from social media text is still a challenging task. This is due to different language styles, informal expression, and the lack of annotated data in many languages. To tackle these issues, we propose, Semi-SMDNet, a strong Semi-Supervised Multilingual Depression detection Network. It combines teacher-student pseudo-labelling, ensemble learning, and augmentation of data. Our framework uses a group of teacher models. Their predictions come together through soft voting. An uncertainty-based threshold filters out low-confidence pseudo-labels to reduce noise and improve learning stability. We also use a confidence-weighted training method that focuses on reliable pseudo-labelled samples. This greatly boosts robustness across languages. Tests on Arabic, Bangla, English, and Spanish datasets show that our approach consistently beats strong baselines. It significantly reduces the performance gap between settings that have plenty of resources and those that do not. Detailed experiments and studies confirm that our framework is effective and can be used in various situations. This shows that it is suitable for scalable, cross-language mental health monitoring where labelled resources are limited.
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。
