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Register a new userEmotion-Infused Models for Explainable Psychological Stress Detection
نماذج مشغولة العاطفة للكشف عن الإجهاد النفسي الشرح
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مشكلة الكشف عن الإجهاد النفسي في الوظائف عبر الإنترنت، وعلى نطاق أوسع، من اكتشاف الناس في محنة أو في حاجة إلى مساعدة، هو تطبيق حساس له القدرة على تفسير النماذج أمر حيوي.هنا، نقدم العمل في استكشاف استخدام مهمة ذات صلة من الناحية الدلوية، والكشف عن المشاعر، من أجل الكشف عن الإجهاد النفسي غير المختص به بنفس القدر ولكن أكثر قابلية للتفسير ومقارنة مع نموذج الصندوق الأسود.على وجه الخصوص، نستكشف استخدام التعلم متعدد المهام وكذلك طراز اللغة القائمة على العاطفة.مع نماذجنا المخفوعة العاطفة، نرى نتائج مماثلة لتحقيق أحدث بيرت.تبين تحليلنا للكلمات المستخدمة للتنبؤ أن نماذجنا المشنقة لدينا مرآة مكونات نفسية من الإجهاد.
The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital. Here, we present work exploring the use of a semantically related task, emotion detection, for equally competent but more explainable and human-like psychological stress detection as compared to a black-box model. In particular, we explore the use of multi-task learning as well as emotion-based language model fine-tuning. With our emotion-infused models, we see comparable results to state-of-the-art BERT. Our analysis of the words used for prediction show that our emotion-infused models mirror psychological components of stress.
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The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction. Stress and emotion are both human affective states, and stress has proven to have important
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