公开(公告)号：US12032627B2

公开(公告)日：2024-07-09

申请号：US17526806

申请日：2021-11-15

申请人： Microsoft Technology Licensing, LLC

发明人： Jinchao Li ,  Lars H. Liden ,  Baolin Peng ,  Thomas Park ,  Swadheen Kumar Shukla ,  Jianfeng Gao

IPC分类号： G06F16/60 ,  G06F16/63 ,  G06N20/00

CPC分类号： G06F16/63 ,  G06N20/00

摘要： Systems and methods are provided for determining a response to a query in a dialog. An entity extractor extracts rules and conditions associated with the query and determines a particular task. The disclosed technology generates a transformer-based dialog embedding by pre-training a transformer using dialog corpora including a plurality of tasks. A task-specific classifier generates a first set of candidate responses based on rules and conditions associated with the task. The transformer-based dialog embedding generates a second set of candidate responses to the query. The classifier accommodates changes made to a task by an interactive dialog editor as machine teaching. A response generator generates a response based on the first and second sets of candidate responses using an optimization function. The disclosed technology leverages both a data-driven, generative model (a transformer) based on dialog corpora and a user-driven, task-specific rule-based classifier that accommodating updates in rules and conditions associated with a particular task.

公开(公告)号：US11961509B2

公开(公告)日：2024-04-16

申请号：US16839308

申请日：2020-04-03

申请人： Microsoft Technology Licensing, LLC

发明人： Swadheen Kumar Shukla ,  Lars Hasso Liden ,  Thomas Park ,  Matthew David Mazzola ,  Shahin Shayandeh ,  Jianfeng Gao ,  Eslam Kamal Abdelreheem

IPC分类号： G10L15/00 ,  G06N3/044 ,  G06N3/049 ,  G06N3/08 ,  G10L15/06 ,  G10L15/16 ,  G10L15/22 ,  G10L25/30

CPC分类号： G10L15/063 ,  G06N3/044 ,  G06N3/049 ,  G06N3/08 ,  G10L15/16 ,  G10L15/22 ,  G10L25/30 ,  G10L2015/0635 ,  G10L2015/225

摘要： Methods and systems are disclosed for improving dialog management for task-oriented dialog systems. The disclosed dialog builder leverages machine teaching processing to improve development of dialog managers. In this way, the dialog builder combines the strengths of both rule-based and machine-learned approaches to allow dialog authors to: (1) import a dialog graph developed using popular dialog composers, (2) convert the dialog graph to text-based training dialogs, (3) continuously improve the trained dialogs based on log dialogs, and (4) generate a corrected dialog for retraining the machine learning.