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Register a new userMicromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health
ميكرومودال لأنظمة فعالة وتفسير وقابلة لإعادة الاستخدام: دراسة حالة حول الصحة العقلية
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العديد من النماذج الإحصائية لها دقة عالية على معايير الاختبار، ولكنها ليست تفسيرها، لا يمكن إعادة استخدام النضال في سيناريوهات الموارد المنخفضة، ولا يمكن إعادة استخدامها لمهام متعددة، ولا يمكن دمج خبرات المجال بسهولة.هذه العوامل تحد من استخدامها، لا سيما في إعدادات الصحة العقلية، حيث من الصعب التعليق على مجموعات البيانات والنواتج النموذجية لها تأثير كبير.نقدم بنية micromodel لمعالجة هذه التحديات.يسمح نهجنا الباحثين ببناء تمثيلات قابلة للتفسير تضمين معرفة المجال وتقديم توضيحات خلال عملية قرار النموذج.نوضح الفكرة على مهام الصحة العقلية المتعددة: تصنيف الاكتئاب، تصنيف PTSD، وتقييم المخاطر الانتحارية.تنتج أنظمتنا باستمرار نتائج قوية، حتى في سيناريوهات الموارد المنخفضة، وهي أكثر تفسيرا من الأساليب البديلة.
Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise. These factors limit their use, particularly in settings such as mental health, where it is difficult to annotate datasets and model outputs have significant impact. We introduce a micromodel architecture to address these challenges. Our approach allows researchers to build interpretable representations that embed domain knowledge and provide explanations throughout the model's decision process. We demonstrate the idea on multiple mental health tasks: depression classification, PTSD classification, and suicidal risk assessment. Our systems consistently produce strong results, even in low-resource scenarios, and are more interpretable than alternative methods.
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Developing documentation guidelines and easy-to-use templates for datasets and models is a challenging task, especially given the variety of backgrounds, skills, and incentives of the people involved in the building of natural language processing (NL
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