- Patent Title: Parallel-hierarchical model for machine comprehension on small data
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Application No.: US15461250Application Date: 2017-03-16
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Publication No.: US10691999B2Publication Date: 2020-06-23
- Inventor: Adam Trischler , Zheng Ye , Xingdi Yuan , Philip Bachman
- Applicant: Maluuba Inc.
- Applicant Address: CA Toronto
- Assignee: Maluuba Inc.
- Current Assignee: Maluuba Inc.
- Current Assignee Address: CA Toronto
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G06F40/30 ; G06F40/284 ; G06N5/02 ; G06N20/10 ; G06N5/04

Abstract:
Examples of the present disclosure provide systems and methods relating to a machine comprehension test with a learning-based approach, harnessing neural networks arranged in a parallel hierarchy. This parallel hierarchy enables the model to compare the passage, question, and answer from a variety of perspectives, as opposed to using a manually designed set of features. Perspectives may range from the word level to sentence fragments to sequences of sentences, and networks operate on word-embedding representations of text. A training methodology for small data is also provided.
Public/Granted literature
- US20170270409A1 PARALLEL-HIERARCHICAL MODEL FOR MACHINE COMPREHENSION ON SMALL DATA Public/Granted day:2017-09-21
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