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Incorporating knowledge bases (KB) into end-to-end task-oriented dialoguesystems is challenging, since it requires to properly represent the entity ofKB, which is associated with its KB context and dialogue context. The existingworks represent the entity with only perceiving a part of its KB context, whichcan lead to the less effective representation due to the information loss, andadversely favor KB reasoning and response generation. To tackle this issue, weexplore to fully contextualize the entity representation by dynamicallyperceiving all the relevant entities} and dialogue history. To achieve this, wepropose a COntext-aware Memory Enhanced Transformer framework (COMET), whichtreats the KB as a sequence and leverages a novel Memory Mask to enforce theentity to only focus on its relevant entities and dialogue history, whileavoiding the distraction from the irrelevant entities. Through extensiveexperiments, we show that our COMET framework can achieve superior performanceover the state of the arts.

Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems