Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
November 01, 2017 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Anjishnu Kumar, Arpit Gupta, Julian Chan, Sam Tucker, Bjorn Hoffmeister, Markus Dreyer, Stanislav Peshterliev, Ankur Gandhe, Denis Filiminov, Ariya Rastrow, Christian Monson, Agnika Kumar
arXiv ID
1711.00549
Category
cs.CL: Computation & Language
Cross-listed
cs.AI,
cs.NE,
cs.SE
Citations
68
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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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