The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 03, 2020

Filed:

Nov. 06, 2017
Applicant:

Semantic Machines, Inc., Newton, MA (US);

Inventors:

Percy Shuo Liang, Palo Alto, CA (US);

Daniel Klein, Orinda, CA (US);

Laurence Steven Gillick, Newton, MA (US);

Jordan Rian Cohen, Kure Beach, NC (US);

Linda Kathleen Arsenault, Chelmsford, MA (US);

Joshua James Clausman, Somerville, MA (US);

Adam David Pauls, Berkeley, CA (US);

David Leo Wright Hall, Berkeley, CA (US);

Assignee:

Semantic Machines, Inc., Newton, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/35 (2020.01); G10L 15/22 (2006.01); G06F 40/169 (2020.01); G06N 20/00 (2019.01); G10L 15/06 (2013.01); G06F 40/20 (2020.01); G06F 40/30 (2020.01); G06F 40/205 (2020.01); G06F 3/0482 (2013.01); G06F 7/08 (2006.01);
U.S. Cl.
CPC ...
G06F 40/169 (2020.01); G06F 3/0482 (2013.01); G06F 7/08 (2013.01); G06F 40/20 (2020.01); G06F 40/205 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G10L 15/063 (2013.01); G10L 15/22 (2013.01); G10L 2015/0638 (2013.01);
Abstract

A data collection system is based on a general set of dialogue acts which are derived from a database schema. Crowd workers perform two types of tasks: (i) identification of sensical dialogue paths and (ii) performing context-dependent paraphrasing of these dialogue paths into real dialogues. The end output of the system is a set of training examples of real dialogues which have been annotated with their logical forms. This data can be used to train all three components of the dialogue system: (i) the semantic parser for understanding context-dependent utterances, (ii) the dialogue policy for generating new dialogue acts given the current state, and (iii) the generation system for both deciding what to say and how to render it in natural language.


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