Minimization of computational demands in model agnostic cross-lingual transfer with neural task representations as weak supervision
US11556776B2 · kind B2 · utility
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Key dates
| Filing date | Oct 18, 2018 |
| Grant date | Jan 17, 2023 |
| Priority date | — |
| Expiry date | Sep 18, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/096
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A task agnostic framework for neural model transfer from a first language to a second language, that can minimize computational and monetary costs by accurately forming predictions in a model of the second language by relying on only a labeled data set in the first language, a parallel data set between both languages, a labeled loss function, and an unlabeled loss function. The models may be trained jointly or in a two-stage process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.