公开(公告)号：EP3304341A1

公开(公告)日：2018-04-11

申请号：EP16733750.0

申请日：2016-05-06

申请人： Microsoft Technology Licensing, LLC

发明人： GALLEY, Michel ,  SORDONI, Alessandro ,  BROCKETT, Christopher John ,  GAO, Jianfeng ,  DOLAN, William Brennan ,  JI, Yangfeng ,  AULI, Michael ,  MITCHELL, Margaret Ann ,  QUIRK, Christopher Brian

IPC分类号： G06F17/28

CPC分类号： H04L51/02 ,  G06F17/2881 ,  H04L51/10 ,  H04L51/26

摘要： Examples are generally directed towards automatic assessment of machine generated conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of multi-reference responses. A response in the set of multi-reference responses includes it context-message data pair and rating. The rating indicates a quality of the response relative to the context-message data pair. A response assessment engine generates a metric score for a machine-generated response based on an assessment metric and the set of multi-reference responses. The metric score indicates a quality of the machine-generated conversational response relative to a user-generated message and a context of the user-generated message. A response generation system of a computing device, such as a digital assistant, is optimized and adjusted based on the metric score to improve the accuracy, quality, and relevance of responses output to the user.

公开(公告)号：EP3304340A1

公开(公告)日：2018-04-11

申请号：EP16723226.3

申请日：2016-05-06

申请人： Microsoft Technology Licensing, LLC

发明人： GALLEY, Michel ,  SORDONI, Alessandro ,  BROCKETT, Christopher John ,  GAO, Jianfeng ,  DOLAN, William Brennan ,  JI, Yangfeng ,  AULI, Michael ,  MITCHELL, Margaret Ann ,  NIE, Jian-Yun

IPC分类号： G06F17/28

CPC分类号： H04L51/02 ,  G06F17/2881 ,  G06N3/0445 ,  G06N3/0454

摘要： Examples are generally directed towards context-sensitive generation of conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of training context-message-response n-tuples. A response generation engine is trained on the set of training context-message-response n-tuples. The trained response generation engine automatically generates a context-sensitive response based on a user generated input message and conversational context data. A digital assistant utilizes the trained response generation engine to generate context-sensitive, natural language responses that are pertinent to user queries.