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. 10, 2018

Filed:

Mar. 18, 2016
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Kartik Audhkhasi, White Plains, NY (US);

Bhuvana Ramabhadran, Mount Kisco, NY (US);

Abhinav Sethy, Chappaqua, NY (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/27 (2006.01); G06F 17/28 (2006.01); G10L 25/30 (2013.01); G10L 15/16 (2006.01);
U.S. Cl.
CPC ...
G06F 17/28 (2013.01); G06F 17/2775 (2013.01); G06F 17/2785 (2013.01); G06F 17/274 (2013.01); G10L 15/16 (2013.01); G10L 25/30 (2013.01);
Abstract

A mechanism is provided in a data processing system for external word embedding neural network language models. The mechanism configures the data processing system with an external word embedding neural network language model that accepts as input a sequence of words and predicts a current word based on the sequence of words. The external word embedding neural network language model combines an external embedding matrix to a history word embedding matrix and a prediction word embedding matrix of the external word embedding neural network language model. The mechanism receives a sequence of input words by the data processing system. The mechanism applies a plurality of previous words in the sequence of input words as inputs to the external word embedding neural network language model. The external word embedding neural network language model generates a predicted current word based on the plurality of previous words. The mechanism processes a current word in the sequence of input words based on the predicted current word generated by the external word embedding neural network language model.


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