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. 20, 2021

Filed:

Jan. 23, 2017
Applicant:

Koninklijke Philips N.v., Eindhoven, NL;

Inventors:

Sheikh Sadid Al Hasan, Cambridge, MA (US);

Bo Liu, Cambridge, MA (US);

Oladimeji Feyisetan Farri, Yorktown Heights, NY (US);

Junyi Liu, Windham, NH (US);

Aaditya Prakash, Waltham, MA (US);

Assignee:

Koninklijke Philips N.V., Eindhoven, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06F 40/56 (2020.01); G06F 40/247 (2020.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/247 (2020.01); G06F 40/56 (2020.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06N 5/02 (2013.01);
Abstract

The present disclosure pertains to a paraphrase generation system. The system comprises one or more hardware processors and/or other components. The system is configured to obtain a training corpus. The training corpus comprises language and known paraphrases of the language. The system is configured to generate, based on the training corpus, a word-level attention-based model and a character-level attention-based model. The system is configured to provide one or more candidate paraphrases of a natural language input based on both the word-level and character-level attention-based models. The word-level attention-based model is a word-level bidirectional long short term memory (LSTM) network and the character-level attention-based model is a character-level bidirectional LSTM network. The word-level and character level LSTM networks are generated based on words and characters in the training corpus. In some embodiments, the LSTM networks are stacked residual LSTM networks comprising residual connections between stacked layers of a given LSTM network.


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