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People are becoming increasingly comfortable using Digital Assistants (DAs)to interact with services or connected objects. However, for non-programmingusers, the available possibilities for customizing their DA are limited and donot include the possibility of teaching the assistant new tasks. To make themost of the potential of DAs, users should be able to customize assistants byinstructing them through Natural Language (NL). To provide suchfunctionalities, NL interpretation in traditional assistants should beimproved: (1) The intent identification system should be able to recognize newforms of known intents, and to acquire new intents as they are expressed by theuser. (2) In order to be adaptive to novel intents, the Natural LanguageUnderstanding module should be sample efficient, and should not rely on apretrained model. Rather, the system should continuously collect the trainingdata as it learns new intents from the user. In this work, we propose AidMe(Adaptive Intent Detection in Multi-Domain Environments), a user-in-the-loopadaptive intent detection framework that allows the assistant to adapt to itsuser by learning his intents as their interaction progresses. AidMe builds itsrepertoire of intents and collects data to train a model of semantic similarityevaluation that can discriminate between the learned intents and autonomouslydiscover new forms of known intents. AidMe addresses two major issues - intentlearning and user adaptation - for instructable digital assistants. Wedemonstrate the capabilities of AidMe as a standalone system by comparing itwith a one-shot learning system and a pretrained NLU module through simulationsof interactions with a user. We also show how AidMe can smoothly integrate toan existing instructable digital assistant.

User-in-the-loop Adaptive Intent Detection for Instructable Digital Assistant