Recognizing the Irrelevant

Part of any conversation is the means of filtering out value and movement toward goals.

Learning to Recognize the Irrelevant

By Young-Bum Kim Alexa Dev
Alexa  Alexa research  Alexa science

A central task of natural-language-understanding systems, like the ones that power Alexa, is domain classification, or determining the general subject of a user’s utterances. Voice services must make finer-grained determinations, too, such as the particular actions that a customer wants executed. But domain classification makes those determinations much more efficient, by narrowing the range of possible interpretations.

Sometimes, though, an Alexa customer might say something that doesn’t fit into any domain. It may be an honest request for a service that doesn’t exist yet, or it might be a case of the customer’s thinking out loud: “Oh wait, that’s not what I wanted.”

If a natural-language-understanding (NLU) system tries to assign a domain to an out-of-domain utterance, the result is likely to be a nonsensical response. Worse, if the NLU system is tracking the conversation, so that it can use contextual information to improve performance, the interpolation of an irrelevant domain can disrupt its sequence of inferences. Getting back on track can be both time consuming and, for the user, annoying.  .... "