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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
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.
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公开(公告)号：EP4248301A1
公开(公告)日：2023-09-27
申请号：EP21807341.9
申请日：2021-10-05
发明人： DOLAN, William B. , WU, Zeqiu , GALLEY, Michel , ZHANG, Yizhe , LI, Zhang , BROCKETT, Christopher John
IPC分类号： G06F3/0482 , G06F40/186
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公开(公告)号：EP4359991A1
公开(公告)日：2024-05-01
申请号：EP22729983.1
申请日：2022-05-20
IPC分类号： G06F40/166 , G06F40/56 , G06F40/44 , G06F40/169
CPC分类号： G06F40/166 , G06F40/56 , G06F40/44 , G06F40/169
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公开(公告)号：EP4457685A1
公开(公告)日：2024-11-06
申请号：EP22847325.2
申请日：2022-12-07
IPC分类号： G06F40/151 , G06F40/166 , G06F40/197
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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
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.
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