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Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding

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arxiv 1711.00549 v4 pith:RDRL4U75 submitted 2017-11-01 cs.CL cs.AIcs.NEcs.SE

Anjishnu Kumar , Arpit Gupta , Julian Chan , Sam Tucker , Bjorn Hoffmeister , Markus Dreyer , Stanislav Peshterliev , Ankur Gandhe
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Denis Filiminov Ariya Rastrow Christian Monson Agnika Kumar
This is my paper · ORCID
classification cs.CLcs.AIcs.NEcs.SE
keywords amazondevelopersalexaarchitecturelanguageskillssoftwarespoken
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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