Researchers at Amazon have managed to improve Alexa’s ability to choose third-party apps, or skills, by using a novel data representation technique.
In a blog post and accompanying paper (“Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding”), Young-Bum Kim, an Amazon science leader in the Seattle company’s Alexa AI division, and team describe a scheme devised for natural language tasks that can cut Alexa’s skill selection error rate by 40 percent.
Their work will be presented at the IEEE Spoken Language Technologies conference in Athens, Greece later this month, and comes on the heels of research last week that was shown to improve Alexa’s speech recognition up to 15 percent.
“Natural language understanding (NLU) systems, for instance, rarely take raw text as inputs.
Using embeddings rather than raw text has been shown time and again to improve performance on particular NLU tasks.”
The new representation method takes advantage of the way Alexa handles requests.