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1. (WO2018102240) JOINT LANGUAGE UNDERSTANDING AND DIALOGUE MANAGEMENT

Pub. No.:    WO/2018/102240    International Application No.:    PCT/US2017/063217
Publication Date: Fri Jun 08 01:59:59 CEST 2018 International Filing Date: Tue Nov 28 00:59:59 CET 2017
IPC: G10L 15/06
G10L 15/22
Applicants: MICROSOFT TECHNOLOGY LICENSING, LLC
Inventors: LI, Xiujun
CROOK, Paul Anthony
DENG, Li
GAO, Jianfeng
CHEN, Yun-Nung
YANG, Xuesong
Title: JOINT LANGUAGE UNDERSTANDING AND DIALOGUE MANAGEMENT
Abstract:
A processing unit can operate an end-to-end recurrent neural network (RNN) with limited contextual dialogue 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 dialogue 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, and perform slot tagging, and make system action predictions based on user input, e.g., utterances across a number of domains.