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Spoken language understanding (SLU) systems in conversational AI agents oftenexperience errors in the form of misrecognitions by automatic speechrecognition (ASR) or semantic gaps in natural language understanding (NLU).These errors easily translate to user frustrations, particularly so inrecurrent events e.g. regularly toggling an appliance, calling a frequentcontact, etc. In this work, we propose a query rewriting approach by leveragingusers' historically successful interactions as a form of memory. We present aneural retrieval model and a pointer-generator network with hierarchicalattention and show that they perform significantly better at the queryrewriting task with the aforementioned user memories than without. We alsohighlight how our approach with the proposed models leverages the structuraland semantic diversity in ASR's output towards recovering users' intents.

Personalized Query Rewriting in Conversational AI Agents