Parallel-hierarchical model for machine comprehension on small data
US10691999B2 · kind B2 · utility
2Cited by
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20Claims
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Key dates
| Filing date | Mar 16, 2017 |
| Grant date | Jun 23, 2020 |
| Priority date | — |
| Expiry date | Nov 14, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.