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Register a new userContextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems
محول قواعد المعرفة مع محول نظم الحوار الموجه نحو المهام المنتهي
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إن دمج قواعد المعرفة (KB) في أنظمة الحوار الموجهة نحو المهام الواحد أمرا صعبا، لأنها تتطلب تمثيل كيان KB بشكل صحيح، وهو مرتبط بسياق KB وحالات الحوار. تمثل الأعمال الحالية الكيان مع إدراك جزء من سياق KB فقط، والذي يمكن أن يؤدي إلى تمثيل أقل فعالية بسبب فقدان المعلومات، ويلفح سلبا من أجل تناسبي KB وتوليد الاستجابة. لمعالجة هذه المشكلة، نستكشف من السياق بالكامل عن تمثيل الكيان من خلال إدراك جميع الكيانات والحوار ذات الصلة ديناميكيا. لتحقيق ذلك، نقترح، نقترح إطار محول محول في الذاكرة المعززة بالذاكرة (المذنب)، والتي تعامل KB كسلسلة وتزايد قناع ذاكرة جديدة لفرض الكيان على التركيز فقط على كياناتها ذات الصلة وحوار التاريخ، مع تجنب الهاء من الكيانات غير ذات الصلة. من خلال تجارب واسعة، نوضح أن إطار المنزول لدينا يمكن أن يحقق أداء فائقا على حالة الآداب.
Incorporating knowledge bases (KB) into end-to-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue context. The existing works represent the entity with only perceiving a part of its KB context, which can lead to the less effective representation due to the information loss, and adversely favor KB reasoning and response generation. To tackle this issue, we explore to fully contextualize the entity representation by dynamically perceiving all the relevant entities and dialogue history. To achieve this, we propose a COntext-aware Memory Enhanced Transformer framework (COMET), which treats the KB as a sequence and leverages a novel Memory Mask to enforce the entity to only focus on its relevant entities and dialogue history, while avoiding the distraction from the irrelevant entities. Through extensive experiments, we show that our COMET framework can achieve superior performance over the state of the arts.
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