SCALING STATISTICAL LANGUAGE UNDERSTANDING SYSTEMS ACROSS DOMAINS AND INTENTS

摘要

A scalable statistical language understanding (SLU) system uses a fixed number of understanding models that scale across domains and intents (i.e. single vs. multiple intents per utterance). For each domain added to the SLU system, the fixed number of existing models is updated to reflect the newly added domain. Information that is already included in the existing models and the corresponding training data may be re-used. The fixed models may include a domain detector model, an intent action detector model, an intent object detector model and a slot/entity tagging model. A domain detector identifies different domains identified within an utterance. All/portion of the detected domains are used to determine associated intent actions. For each determined intent action, one or more intent objects are identified. Slot/entity tagging is performed using the determined domains, intent actions, and intent object detector.

主权项

1. A method for scaling a language understanding system, comprising:
receiving an utterance using the language understanding system that uses a model that spans domains and intents; detecting domains that are associated with the utterance; detecting actions from the utterance that are associated with the detected domains; detecting objects from the utterance that are associated with the detected domains; and performing slot filling based on detected objects and intents across the spanned domains and intents.