公开(公告)号：US20200334520A1

公开(公告)日：2020-10-22

申请号：US16443440

申请日：2019-06-17

申请人： Microsoft Technology Licensing, LLC

发明人： Weizhu CHEN ,  Pengcheng HE ,  Xiaodong LIU ,  Jianfeng GAO

IPC分类号： G06N3/04 ,  G06N3/08 ,  G06F17/27

摘要： This document relates to architectures and training procedures for multi-task machine learning models, such as neural networks. One example method involves providing a multi-task machine learning model having one or more shared layers and two or more task-specific layers. The method can also involve performing a pretraining stage on the one or more shared layers using one or more unsupervised prediction tasks. The method can also involve performing a tuning stage on the one or more shared layers and the two or more task-specific layers using respective task-specific objectives

公开(公告)号：US20200302019A1

公开(公告)日：2020-09-24

申请号：US16362313

申请日：2019-03-22

申请人： Microsoft Technology Licensing, LLC

发明人： Matthew Brigham HALL ,  Weizhu CHEN ,  Junyan CHEN ,  Pengcheng HE ,  Yu ZHAO ,  Yi-Min WANG ,  Yuting SUN ,  Zheng CHEN ,  Katherine Winant OSBORNE

IPC分类号： G06F17/27 ,  G06N20/00

摘要： A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.