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:
Jul. 30, 2024

Filed:

Sep. 01, 2021
Applicant:

Nec Laboratories America, Inc., Princeton, NJ (US);

Inventors:

Xuchao Zhang, Elkridge, MD (US);

Yanchi Liu, Monmouth Junction, NJ (US);

Bo Zong, West Windsor, NJ (US);

Wei Cheng, Princeton Junction, NJ (US);

Haifeng Chen, West Windsor, NJ (US);

Junxiang Wang, Fairfax, VA (US);

Assignee:

NEC Corporation, Tokyo, JP;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 40/284 (2020.01); G06F 40/205 (2020.01); G06F 40/295 (2020.01); G06N 3/04 (2023.01);
U.S. Cl.
CPC ...
G06F 40/284 (2020.01); G06F 40/205 (2020.01); G06F 40/295 (2020.01); G06N 3/04 (2013.01);
Abstract

A computer-implemented method is provided for cross-lingual transfer. The method includes randomly masking a source corpus and a target corpus to obtain a masked source corpus and a masked target corpus. The method further includes tokenizing, by pretrained Natural Language Processing (NLP) models, the masked source corpus and the masked target corpus to obtain source tokens and target tokens. The method also includes transforming the source tokens and the target tokens into a source dependency parsing tree and a target dependency parsing tree. The method additionally includes inputting the source dependency parsing tree and the target dependency parsing tree into a graph encoder pretrained on a translation language modeling task to extract common language information for transfer. The method further includes fine-tuning the graph encoder and a down-stream network for a specific NLP down-stream task.


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