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Register a new userEnd-to-End Learning of Flowchart Grounded Task-Oriented Dialogs
تعلم نهاية إلى نهاية من الحوار الموجهة نحو الانسحاب
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نقترح مشكلة جديدة في مجال التعلم المنتهي في نهاية الحوار الموجهة نحو الوظيفة (TOD)، حيث يحاكي نظام مربع الحوار وكيل استكشاف الأخطاء وإصلاحها يساعد المستخدم من خلال تشخيص مشكلتهم (على سبيل المثال، السيارة لا تبدأ).ترتكز حوارات هذه الحوار في مخططات المخططات الموسيقية الخاصة بالمجال، والتي من المفترض أن يتبع الوكيل أثناء المحادثة.تعرض مهمتنا تحديات تقنية جديدة من أجل TOD العصبي، مثل التأريض على الكلام إلى مخطط الانسيابي دون عبوات صريحة، في إشارة إلى الصفحات اليدوية الإضافية عندما يطلب المستخدم سؤالا توضيحا، والقدرة على اتباع مخططات انسيابية غير مرئية في وقت الاختبار.نقوم بإصدار مجموعة بيانات (Flodial) تتكون من 2،738 مربعا على 12 نقطة مخاطرة مختلفة لاستكشاف الأخطاء وإصلاحها.نقوم أيضا بتصميم نموذج عصبي، FLONET، والذي يستخدم بنية توليد تعزز استرجاع لتدريب وكيل الحوار.تجد تجاربنا أن الألهام يمكن أن تفعل نقل طلقة صفرية إلى مخططات انسيابية غير مرئية، ويضع خط أساس قوي للبحث في المستقبل.
We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.
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