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MAINTAINING MACHINE LANGUAGE MODEL STATE ACROSS COMMUNICATIONS CHANNELS

机译:跨通信渠道维护机器语言模型状态

摘要

Machine learning models may be used during a communications session to process natural language communications and perform actions relating to the communications session. For example, a machine learning model may be used to provide an automated response to a user, to suggest a completion of text being entered by a user, or to provide information about a relevant resource. Machine learning models may rely on machine learning model data that is updated during a communications session as communications are processed by the machine learning model. To improve the performance of a machine learning model when a user leaves a first communications session and enters a second communications session, the machine learning model data may be stored during a first communications session and then retrieved during the second communications session to initialize a machine learning model for the second communications session.
机译:可以在通信会话期间使用机器学习模型来处理自然语言通信并执行与该通信会话有关的动作。例如,机器学习模型可以用于向用户提供自动响应,建议用户输入的文本的完成或提供有关相关资源的信息。机器学习模型可能依赖于在机器学习模型处理通信时在通信会话期间更新的机器学习模型数据。为了在用户离开第一通信会话并进入第二通信会话时提高机器学习模型的性能,可以在第一通信会话期间存储机器学习模型数据,然后在第二通信会话期间检索机器学习模型数据以初始化机器学习第二个通信会话的模型。

著录项

  • 公开/公告号US2020327892A1

    专利类型

  • 公开/公告日2020-10-15

    原文格式PDF

  • 申请/专利权人 ASAPP INC.;

    申请/专利号US201916503528

  • 申请日2019-07-04

  • 分类号G10L15/26;G06N20;G06F17/27;H04L12/58;

  • 国家 US

  • 入库时间 2022-08-21 11:26:03

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