Continuous learning for natural-language understanding models for assistant systems
US11861315B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 18, 2021 |
| Grant date | Jan 2, 2024 |
| Priority date | — |
| Expiry date | May 27, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L51/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In one embodiment, a method includes receiving a user request to automatically debug a natural-language understanding (NLU) model, accessing a plurality of predicted semantic representations generated by the NLU model, wherein the plurality of predicted semantic representations are associated with a plurality of dialog sessions, respectively, wherein each dialog session is between a user and an assistant xbot associated with the NLU model, generating a plurality of expected semantic representations associated with the plurality of dialog sessions based on an auto-correction model, wherein the auto-correction model is learned from dialog training samples generated based on active learning, identifying incorrect semantic representations of the predicted semantic representations based on a comparison between the predicted semantic representations and the expected semantic representations, and automatically correcting the incorrect semantic representations by replacing them with respective expected semantic representations generated by the auto-correction model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.