Contextual text generation for question answering and text summarization with supervised representation disentanglement and mutual information minimization
US11887008B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 8, 2020 |
| Grant date | Jan 30, 2024 |
| Priority date | — |
| Expiry date | May 23, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/082
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for disentangled data generation include accessing a dataset including pairs, each formed from a given input text structure and a given style label for the input text structures. An encoder is trained to disentangle a sequential text input into disentangled representations, including a content embedding and a style embedding, based on a subset of the dataset, using an objective function that includes a regularization term that minimizes mutual information between the content embedding and the style embedding. A generator is trained to generate a text output that includes content from the style embedding, expressed in a style other than that represented by the style embedding of the text input.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.