Cross-domain multi-task learning for text classification
US10937416B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 1, 2019 |
| Grant date | Mar 2, 2021 |
| Priority date | — |
| Expiry date | Jun 1, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/26
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method includes providing input text to a plurality of multi-task learning (MTL) models corresponding to a plurality of domains. Each MTL model is trained to generate an embedding vector based on the input text. The method further includes providing the input text to a domain identifier that is trained to generate a weight vector based on the input text. The weight vector indicates a classification weight for each domain of the plurality of domains. The method further includes scaling each embedding vector based on a corresponding classification weight of the weight vector to generate a plurality of scaled embedding vectors, generating a feature vector based on the plurality of scaled embedding vectors, and providing the feature vector to an intent classifier that is trained to generate, based on the feature vector, an intent classification result associated with the input text.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.