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Register a new userSELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers
selfexplain: هندسة موضحة ذاتيا من مصنفات النص العصبي
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نحن نقدم SelfExPlain، وهو نموذج جديد يشرح ذاتيا يفسر تنبؤات تصنيف النص باستخدام المفاهيم القائمة على العبارة.SelfExplain تزويد الأقراص العصبية الموجودة من خلال إضافة (1) طبقة مخصصة عالمية تحدد المفاهيم الأكثر نفوذا في مجموعة التدريب لعينة معينة و (2) طبقة قابلة للتفسير محليا تهدئ مساهمة كل مفهوم إدخال محلي عن طريق الحوسبة درجة الأهميةبالنسبة إلى التسمية المتوقعة.تظهر التجارب عبر خمسة مجموعات بيانات تصنيف نصية أن SelfExPlain يسهل الترجمة الشفوية دون التضحية بالأداء.الأهم من ذلك، تفسيرات من إظهار نفس إظهار الكفاية عن التنبؤات النموذجية وترى أنها كافية وجديرة بالثقة ومفهومة من قبل القضاة البشرية مقارنة مع خطوط الأساس المستخدمة على نطاق واسع.
We introduce SelfExplain, a novel self-explaining model that explains a text classifier's predictions using phrase-based concepts. SelfExplain augments existing neural classifiers by adding (1) a globally interpretable layer that identifies the most influential concepts in the training set for a given sample and (2) a locally interpretable layer that quantifies the contribution of each local input concept by computing a relevance score relative to the predicted label. Experiments across five text-classification datasets show that SelfExplain facilitates interpretability without sacrificing performance. Most importantly, explanations from SelfExplain show sufficiency for model predictions and are perceived as adequate, trustworthy and understandable by human judges compared to existing widely-used baselines.
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