Controlled text generation with supervised representation disentanglement and mutual information minimization
US12045727B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 8, 2020 |
| Grant date | Jul 23, 2024 |
| Priority date | — |
| Expiry date | May 20, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/082
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method is provided for disentangled data generation. The method includes accessing, by a bidirectional Long Short-Term Memory (LSTM) with a multi-head attention mechanism, a dataset including a plurality of pairs each formed from a given one of a plurality of input text structures and given one of a plurality of style labels for the plurality of input text structures. The method further includes training the bidirectional LSTM as an encoder to disentangle a sequential text input into disentangled representations comprising a content embedding and a style embedding based on a subset of the dataset. The method also includes training a unidirectional LSTM as a decoder to generate a next text structure prediction for the sequential text input based on previously generated text structure information and a current word, from a disentangled representation with the content embedding and the style embedding.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.