Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
It addresses the problem of making SLU accessible and efficient for third-party developers and researchers, though it appears incremental as an extension of existing infrastructure.
The paper tackles the challenge of building a scalable and flexible spoken language understanding (SLU) architecture for Amazon's Alexa Skills Kit, enabling robust learning from small datasets and supporting over 25,000 deployed skills.
This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development Kit (SDK) that enables developers to extend the capabilities of Amazon's virtual assistant, Alexa. At Amazon, the infrastructure powers over 25,000 skills deployed through the ASK, as well as AWS's Amazon Lex SLU Service. The ASK emphasizes flexibility, predictability and a rapid iteration cycle for third party developers. It imposes inductive biases that allow it to learn robust SLU models from extremely small and sparse datasets and, in doing so, removes significant barriers to entry for software developers and dialogue systems researchers.