Invention Grant
- Patent Title: Training encoder model and/or using trained encoder model to determine responsive action(s) for natural language input
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Application No.: US16611725Application Date: 2018-12-14
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Publication No.: US10783456B2Publication Date: 2020-09-22
- Inventor: Brian Strope , Yun-hsuan Sung , Wangqing Yuan
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: GOOGLE LLC
- Current Assignee: GOOGLE LLC
- Current Assignee Address: US CA Mountain View
- Agency: Middleton Reutlinger
- International Application: PCT/US2018/065727 WO 20181214
- International Announcement: WO2019/118864 WO 20190620
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F16/35 ; G06F16/332 ; G06F16/33 ; G06N5/04

Abstract:
Systems, methods, and computer readable media related to: training an encoder model that can be utilized to determine semantic similarity of a natural language textual string to each of one or more additional natural language textual strings (directly and/or indirectly); and/or using a trained encoder model to determine one or more responsive actions to perform in response to a natural language query. The encoder model is a machine learning model, such as a neural network model. In some implementations of training the encoder model, the encoder model is trained as part of a larger network architecture trained based on one or more tasks that are distinct from a “semantic textual similarity” task for which the encoder model can be used.
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