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:
Jan. 02, 2024

Filed:

Jun. 18, 2021
Applicant:

Meta Platforms, Inc., Menlo Park, CA (US);

Inventors:

Pooja Sethi, Kent, WA (US);

Denis Savenkov, Redmond, WA (US);

Yue Liu, Belmont, MA (US);

Alexander Kolmykov-Zotov, Sammamish, WA (US);

Ahmed Aly, Kenmore, WA (US);

Assignee:

Meta Platforms, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06F 40/30 (2020.01); G06F 1/3206 (2019.01); G06F 3/01 (2006.01); G06F 3/04815 (2022.01); G06N 5/02 (2023.01); G06N 5/046 (2023.01); G06T 19/00 (2011.01); G06T 19/20 (2011.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 1/3206 (2013.01); G06F 3/011 (2013.01); G06F 3/04815 (2013.01); G06N 5/02 (2013.01); G06N 5/046 (2013.01); G06T 19/006 (2013.01); G06T 19/20 (2013.01); G06T 2219/2004 (2013.01);
Abstract

In one embodiment, a method includes receiving a user request to automatically debug a natural-language understanding (NLU) model, accessing a plurality of predicted semantic representations generated by the NLU model, wherein the plurality of predicted semantic representations are associated with a plurality of dialog sessions, respectively, wherein each dialog session is between a user and an assistant xbot associated with the NLU model, generating a plurality of expected semantic representations associated with the plurality of dialog sessions based on an auto-correction model, wherein the auto-correction model is learned from dialog training samples generated based on active learning, identifying incorrect semantic representations of the predicted semantic representations based on a comparison between the predicted semantic representations and the expected semantic representations, and automatically correcting the incorrect semantic representations by replacing them with respective expected semantic representations generated by the auto-correction model.


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