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公开(公告)号：US10091140B2
公开(公告)日：2018-10-02
申请号：US14726562
申请日：2015-05-31
发明人： Michel Galley , Alessandro Sordoni , Christopher John Brockett , Jianfeng Gao , William Brennan Dolan , Yangfeng Ji , Michael Auli , Margaret Ann Mitchell , Jian-Yun Nie
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：US20230385320A1
公开(公告)日：2023-11-30
申请号：US17836456
申请日：2022-06-09
发明人： Weixin CAI , Si-Qing Chen , Michel Galley , William Brennan Dolan , Christopher J. Brockett , Zhang Li , Warren A. Aldred , Xinyu He , Jesse Alexander Freitas , Kaushik Ramaiah Narayanan
IPC分类号： G06F16/338 , G06F16/332 , G06F16/335 , G06K9/62 , G06F40/30 , G06F40/253 , G06N20/00
CPC分类号： G06F16/338 , G06F16/3326 , G06F16/335 , G06K9/6215 , G06F40/30 , G06F40/253 , G06N20/00
摘要： Systems and methods are directed to generating content that is contextually relevant in a writing style of a user. In example embodiments, a plurality of logical inputs regarding a topic is received in bullet point format. A content generator generates draft content using machine learning (ML) models. The generating comprises identifying a writing style of the user by applying the plurality of logical inputs to a first ML model, determining a context and direction for the draft content using a second ML model, and based on the plurality of logical inputs, the identified writing style, and the context and direction, generating at least one paragraph of draft content in the writing style of the user that follows an outline associated with the bullet point format and comprises a same context and direction as the plurality of logical inputs. The draft content is then presented at a client device.
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公开(公告)号：US10536402B2
公开(公告)日：2020-01-14
申请号：US16112611
申请日：2018-08-24
发明人： Michel Galley , Alessandro Sordoni , Christopher John Brockett , Jianfeng Gao , William Brennan Dolan , Yangfeng Ji , Michael Auli , Margaret Ann Mitchell , Jian-Yun Nie
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：US20190354594A1
公开(公告)日：2019-11-21
申请号：US15984356
申请日：2018-05-20
发明人： Jonathan Burgess Foster , Tulasi Menon , William Brennan Dolan , Radhakrishnan Srikanth , Sai Tulasi Neppali , Michel Galley , Christopher John Brockett , Parag Agrawal , Rohan Kulkarni , Ronald Kevin Owens , Deborah Briana Harrison
摘要： Conversations can be generated automatically based on any given persona. The conversations can be produced by a language generation model that automatically generates persona-based language in response to a message, wherein a persona identifies a type of person with role specific characteristics. After a language generation model is acquired, for example by identifying a predefined model or generating a new model, the language generation model can be provisioned for use by a conversational agent, such as a chatbot, to enhance the functionality of the conversational agent.
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公开(公告)号：US11741306B2
公开(公告)日：2023-08-29
申请号：US16817124
申请日：2020-03-12
摘要： A controllable grounded response generation framework includes a machine learning model, a grounding interface, and a control interface. The machine learning model is trained to output computer-generated text based on input text. The grounding interface is useable by the machine learning model to access a grounding source including information related to the input text. The control interface is useable by the machine learning model to recognize a control signal. The machine learning model is configured to include information from the grounding source in the computer-generated text and focus the computer-generated text based on the control signal.
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公开(公告)号：US11429779B2
公开(公告)日：2022-08-30
申请号：US16459576
申请日：2019-07-01
发明人： Zhang Li , Domenic Joseph Cipollone , Maria Isabel Carpenter , Juhi Amitkumar Naik , Susan Michele Hendrich , Michael Wilson Daniels , William Brennan Dolan , Christopher Brian Quirk , Christopher John Brockett , Alice Yingming Lai
IPC分类号： G06F40/166 , G06F3/0482 , G06K9/62 , G06F40/47 , G06F40/295
摘要： A method and system for providing replacement text segments for a given text segment may include receiving a request to provide the replacement text segment for the text segment in the document, examining a content characteristic of the document, and examining at least one of user-specific information, organization-specific information, or non-linguistic features of the document, before identifying at least one replacement text segment for the text segment, via a machine translation system, based on the content characteristic of the document and at least one of the user-specific information, the organization-specific information, or the non-linguistic features of the document. The method and system may include providing the identified replacement text segment for display to a user, receiving an input indicating a user's selection of the identified replacement text segment, and upon receiving the input, replacing the text segment in the document with the identified replacement text segment.
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公开(公告)号：US11126794B2
公开(公告)日：2021-09-21
申请号：US16381965
申请日：2019-04-11
发明人： Zhang Li , Christopher John Brockett , William Brennan Dolan , Christopher Brian Quirk , Alice Yingming Lai , Susan Michele Hendrich , Olivier Gauthier , Kaushik Ramaiah Narayanan , Maria Isabel Carpenter , Juhi Amitkumar Naik , Michael Wilson Daniels
IPC分类号： G06F40/20 , G06F40/253 , G06F40/166 , G06F40/30 , G06F3/0482
摘要： A method for providing targeted rewrites can include receiving a selection of text in a file; generating a set of target rewrites of the selection of text, the set of target rewrites comprising: at least one phrase or sentence having semantic similarity to a phrase or sentence of the selection of text; and a style that corresponds to a particular target style, wherein a target style is a representative style for a genre, profession, or environment; and providing for selection one or more of the target rewrites of the set of target rewrites.
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公开(公告)号：US12131123B2
公开(公告)日：2024-10-29
申请号：US18334065
申请日：2023-06-13
摘要： A controllable grounded response generation framework includes a machine learning model, a grounding interface, and a control interface. The machine learning model is trained to output computer-generated text based on input text. The grounding interface is useable by the machine learning model to access a grounding source including information related to the input text. The control interface is useable by the machine learning model to recognize a control signal. The machine learning model is configured to include information from the grounding source in the computer-generated text and focus the computer-generated text based on the control signal.
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公开(公告)号：US12105747B2
公开(公告)日：2024-10-01
申请号：US17836456
申请日：2022-06-09
发明人： Weixin Cai , Si-Qing Chen , Michel Galley , William Brennan Dolan , Christopher J. Brockett , Zhang Li , Warren A. Aldred , Xinyu He , Jesse Alexander Freitas , Kaushik Ramaiah Narayanan
IPC分类号： G06F16/338 , G06F16/332 , G06F16/335 , G06F18/22 , G06F40/253 , G06F40/30 , G06N20/00
CPC分类号： G06F16/338 , G06F16/3326 , G06F16/335 , G06F18/22 , G06F40/253 , G06F40/30 , G06N20/00
摘要： Systems and methods are directed to generating content that is contextually relevant in a writing style of a user. In example embodiments, a plurality of logical inputs regarding a topic is received in bullet point format. A content generator generates draft content using machine learning (ML) models. The generating comprises identifying a writing style of the user by applying the plurality of logical inputs to a first ML model, determining a context and direction for the draft content using a second ML model, and based on the plurality of logical inputs, the identified writing style, and the context and direction, generating at least one paragraph of draft content in the writing style of the user that follows an outline associated with the bullet point format and comprises a same context and direction as the plurality of logical inputs. The draft content is then presented at a client device.
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公开(公告)号：US09967211B2
公开(公告)日：2018-05-08
申请号：US14726569
申请日：2015-05-31
发明人： Michel Galley , Alessandro Sordoni , Christopher John Brockett , Jianfeng Gao , William Brennan Dolan , Yangfeng Ji , Michael Auli , Margaret Ann Mitchell , Christopher Brian Quirk
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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