首页> 外国专利> GENERATING DIALOGUE RESPONSES IN END-TO-END DIALOGUE SYSTEMS UTILIZING A CONTEXT-DEPENDENT ADDITIVE RECURRENT NEURAL NETWORK

GENERATING DIALOGUE RESPONSES IN END-TO-END DIALOGUE SYSTEMS UTILIZING A CONTEXT-DEPENDENT ADDITIVE RECURRENT NEURAL NETWORK

机译:利用上下文相关的递归神经网络在端到端对话系统中生成对话响应

摘要

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.
机译:本公开涉及用于使用独立的门上下文相关的加性递归神经网络基于接收的话语生成对话响应的系统,方法和非暂时性计算机可读介质。例如,公开的系统可以利用神经网络模型基于接收到的话语来生成对话历史向量,并且可以使用对话历史向量来生成对话响应。独立于门的上下文相关的加性递归神经网络可以删除局部上下文,以降低计算复杂性,并允许在所有时间步长并行计算门。独立门依赖于上下文的加性递归神经网络使用隐藏矢量输出来维护递归神经网络的顺序性质。

著录项

  • 公开/公告号US2020090651A1

    专利类型

  • 公开/公告日2020-03-19

    原文格式PDF

  • 申请/专利权人 ADOBE INC.;

    申请/专利号US201816133190

  • 发明设计人 QUAN TRAN;TRUNG BUI;HUNG BUI;

    申请日2018-09-17

  • 分类号G10L15/22;G10L15/16;G10L15/30;G06N3/04;G06N3/10;

  • 国家 US

  • 入库时间 2022-08-21 11:23:06

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