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Register a new userAdversarial Self-Supervised Learning for Out-of-Domain Detection
التعلم الخماسي الخاضع للإشراف على الكشف خارج نطاق
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اكتشاف النوايا الخارجية (OOD) أمر حاسم لنظام الحوار المنتشر الموجه نحو المهام.ستقوم أساليب الكشف عن OOD السابقة غير المعروضة فقط باستخراج الميزات التمييزية لمختلف النوايا داخل المجال، بينما يمكن للنظيرات الإشرافية التمييز مباشرة من النوايا OOD والمجال ولكنها تتطلب بيانات المسمى الواسعة.من أجل الجمع بين فوائد كلا النوعين، نقترح إطارا تعليميا مختلفا عن علم الذاتي لنموذج الميزات الدلالية التمييزية لكل من النوايا داخل المجال ومؤلبة OOD من البيانات غير المسبقة.علاوة على ذلك، نقدم وحدة عصبية عمومة خصصا لتحسين كفاءة وأغاني التعلم المقاوم للتناقض.تبين التجارب في مجموعات بيانات القياس العامة أن طريقتنا يمكن أن تفوق باستمرار على الأساس مع هامش مهم إحصائيا.
Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system. Previous unsupervised OOD detection methods only extract discriminative features of different in-domain intents while supervised counterparts can directly distinguish OOD and in-domain intents but require extensive labeled OOD data. To combine the benefits of both types, we propose a self-supervised contrastive learning framework to model discriminative semantic features of both in-domain intents and OOD intents from unlabeled data. Besides, we introduce an adversarial augmentation neural module to improve the efficiency and robustness of contrastive learning. Experiments on two public benchmark datasets show that our method can consistently outperform the baselines with a statistically significant margin.
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