Invention Grant
- Patent Title: Generating dialogue responses in end-to-end dialogue systems utilizing a context-dependent additive recurrent neural network
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Application No.: US16133190Application Date: 2018-09-17
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Publication No.: US10861456B2Publication Date: 2020-12-08
- Inventor: Quan Tran , Trung Bui , Hung Bui
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: ADOBE INC.
- Current Assignee: ADOBE INC.
- Current Assignee Address: US CA San Jose
- Agency: Keller Jolley Preece
- Main IPC: G10L15/22
- IPC: G10L15/22 ; G10L15/16 ; G06N3/10 ; G06N3/04 ; G10L15/30

Abstract:
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.
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