Question Answering using trained generative adversarial network based modeling of text
US11481416B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 12, 2018 |
| Grant date | Oct 25, 2022 |
| Priority date | — |
| Expiry date | Aug 25, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L2015/225
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Mechanisms are provided for implementing a Question Answering (QA) system utilizing a trained generator of a generative adversarial network (GAN) that generates a bag-of-ngrams (BoN) output representing unlabeled data for performing a natural language processing operation. The QA system obtains a plurality of candidate answers to a natural language question, where each candidate answer comprises one or more ngrams. For each candidate answer, a confidence score is generated based on a comparison of the one or more ngrams in the candidate answer to ngrams in the BoN output of the generator neural network of the GAN. A final answer to the input natural language question is selected from the plurality of candidate answers based on the confidence scores associated with the candidate answers, and is output.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.