- 专利标题: TRAINING ENCODER MODEL AND/OR USING TRAINED ENCODER MODEL TO DETERMINE RESPONSIVE ACTION(S) FOR NATURAL LANGUAGE INPUT
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申请号: US16611725申请日: 2018-12-14
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公开(公告)号: US20200104746A1公开(公告)日: 2020-04-02
- 发明人: Brian Strope , Yun-hsuan Sung , Wangqing Yuan
- 申请人: Google LLC
- 国际申请: PCT/US2018/065727 WO 20181214
- 主分类号: G06N20/00
- IPC分类号: G06N20/00 ; G06F16/33 ; G06F16/35 ; G06F16/332 ; G06N5/04
摘要:
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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