US 12,131,728 B2
Method and apparatus of training natural language processing model, and method and apparatus of processing natural language
Siyu Ding, Beijing (CN); Chao Pang, Beijing (CN); Shuohuan Wang, Beijing (CN); Yanbin Zhao, Beijing (CN); Junyuan Shang, Beijing (CN); Yu Sun, Beijing (CN); Shikun Feng, Beijing (CN); Hao Tian, Beijing (CN); Hua Wu, Beijing (CN); and Haifeng Wang, Beijing (CN)
Assigned to BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing (CN)
Filed by Beijing Baidu Netcom Science Technology Co., Ltd., Beijing (CN)
Filed on May 31, 2022, as Appl. No. 17/828,773.
Claims priority of application No. 202110747046.X (CN), filed on Jun. 30, 2021.
Prior Publication US 2022/0293092 A1, Sep. 15, 2022
Int. Cl. G10L 15/00 (2013.01); G10L 15/02 (2006.01); G10L 15/06 (2013.01); G10L 15/18 (2013.01)
CPC G10L 15/063 (2013.01) [G10L 15/02 (2013.01); G10L 15/18 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method of training a natural language processing model, comprising:
performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks comprise a plurality of branch tasks;
performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task;
calculating a loss for each branch task according to the first output result for the branch task;
adjusting a parameter of the natural language processing model according to the loss for each branch task; and
determining a second output result for each branch task based on the semantic feature, wherein
the multi-tasks comprise a first branch task for a semantic understanding; and
the determining a second output result for each branch task based on the semantic feature comprises one of:
determining a semantic understanding information for the input text as the second output result for the first branch task based on the semantic feature;
calculating a logical distance between a plurality of statements in the input text as the second output result for the first branch task based on the semantic feature; and
determining a logical order of a plurality of statements in the input text as the second output result for the first branch task based on the semantic feature.