End-to-end memory networks for contextual language understanding
US11449744B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 4, 2016 |
| Grant date | Sep 20, 2022 |
| Priority date | — |
| Expiry date | Jun 25, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A processing unit can extract salient semantics to model knowledge carryover, from one turn to the next, in multi-turn conversations. Architecture described herein can use the end-to-end memory networks to encode inputs, e.g., utterances, with intents and slots, which can be stored as embeddings in memory, and in decoding the architecture can exploit latent contextual information from memory, e.g., demographic context, visual context, semantic context, etc. e.g., via an attention model, to leverage previously stored semantics for semantic parsing, e.g., for joint intent prediction and slot tagging. In examples, architecture is configured to build an end-to-end memory network model for contextual, e.g., multi-turn, language understanding, to apply the end-to-end memory network model to multiple turns of conversational input; and to fill slots for output of contextual, e.g., multi-turn, language understanding of the conversational input. The neural network can be learned using backpropagation from output to input using gradient descent optimization.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.