Joint language understanding and dialogue management using binary classification based on forward and backward recurrent neural network
US10268679B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 2, 2016 |
| Grant date | Apr 23, 2019 |
| Priority date | — |
| Expiry date | Dec 2, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/1822
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A processing unit can operate an end-to-end recurrent neural network (RNN) with limited contextual dialog memory that can be jointly trained by supervised signals-user slot tagging, intent prediction and/or system action prediction. The end-to-end RNN, or joint model has shown advantages over separate models for natural language understanding (NLU) and dialog management and can capture expressive feature representations beyond conventional aggregation of slot tags and intents, to mitigate effects of noisy output from NLU. The joint model can apply a supervised signal from system actions to refine the NLU model. By back-propagating errors associated with system action prediction to the NLU model, the joint model can use machine learning to predict user intent by a binary classification obtained by both forward and backward output, and perform slot tagging, and make system action predictions based on user input, e.g., utterances across a number of domains.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.