通过遮蔽注意力增强抑郁检测的可解释性。
Explainable Depression Detection using Masked Hard Instance Mining
- 用遮蔽关键实例的方法迫使模型关注更多重要特征。
- 在泰语和英语数据集上准确率与可解释性均显著提升。
- 适合需要可信决策过程的心理健康筛查系统。
本文针对文本抑郁检测中可解释性不足的问题，提出遮蔽困难实例挖掘（MHIM）方法。该方法通过有策略地遮蔽模型中的注意力权重，促使模型将注意力分布到更广泛的显著特征上，从而增强预测的可解释性。我们在两种不同语言的数据集上进行了评估：泰语数据集Thai-Maywe和英语数据集DAIC-WOZ。实验结果表明，MHIM在预测准确率和可解释性指标上均有显著提升，验证了其有效性。
原文摘要 · Abstract (English)
This paper addresses the critical need for improved explainability in text-based depression detection. While offering predictive outcomes, current solutions often overlook the understanding of model predictions which can hinder trust in the system. We propose the use of Masked Hard Instance Mining (MHIM) to enhance the explainability in the depression detection task. MHIM strategically masks attention weights within the model, compelling it to distribute attention across a wider range of salient features. We evaluate MHIM on two datasets representing distinct languages: Thai (Thai-Maywe) and English (DAIC-WOZ). Our results demonstrate that MHIM significantly improves performance in terms of both prediction accuracy and explainability metrics.
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