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

TL;DR

This paper details the architecture of the Alexa Skills Kit, a scalable and flexible SLU SDK that enables rapid development of voice skills for Alexa, supporting thousands of skills and small datasets.

Contribution

It introduces a machine learning architecture that facilitates extensible, robust SLU models from minimal data, enhancing developer accessibility and rapid iteration.

Findings

Supports over 25,000 skills deployed on Alexa

Learns robust SLU models from small, sparse datasets

Enables rapid development and iteration for third-party developers

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.