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:
Mar. 31, 2026

Filed:

Mar. 21, 2022
Applicant:

Beijing Baidu Netcom Science Technology Co., Ltd., Beijing, CN;

Inventors:

Shuai Zhang, Beijing, CN;

Lijie Wang, Beijing, CN;

Xinyan Xiao, Beijing, CN;

Yue Chang, Beijing, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/211 (2020.01); G06F 40/284 (2020.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/211 (2020.01); G06F 40/284 (2020.01);
Abstract

Disclosed are a method for training a semantic representation model, a device and a storage medium, which relate to the field of computer technologies, and particularly to the field of artificial intelligence, such as a natural language processing technology, a deep learning technology, or the like. The method for training a semantic representation model includes: obtaining an anchor sample based on a sentence, and obtaining a positive sample and a negative sample based on syntactic information of the sentence; processing the anchor sample, the positive sample and the negative sample using the semantic representation model respectively, so as to obtain an anchor-sample semantic representation, a positive-sample semantic representation and a negative-sample semantic representation; constructing a contrast loss function based on the anchor-sample semantic representation, the positive-sample semantic representation, and the negative-sample semantic representation; and training the semantic representation model based on the contrast loss function.


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