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Systems and methods for slot relation extraction for machine learning task-oriented dialogue systems

机译:用于机器学习的时隙关系提取系统和方法,面向工作的对话系统

摘要

A system and method for implementing slot-relation extraction for a task-oriented dialogue system that includes implementing dialogue intent classification machine learning models that predict a category of dialogue of a single utterance based on an input of utterance data relating to the single utterance, wherein the category of dialogue informs a selection of slot-filling machine learning models; implementing the slot-filling machine learning models that predict slot classification labels for each of a plurality of slots within the utterance based on the input of the utterance data; implementing a slot relation extraction machine learning model that predicts semantic relationship classifications between two or more distinct slots of tokens of the utterance; and generating a response to the single utterance or performing actions in response to the single utterance based on the semantic relationship classifications between the distinct pairings of the two or more distinct slots of the single utterance.
机译:一种用于实现面向任务对话系统的时隙关系的系统和方法,包括实现对话意图分类机学习模型,其基于与单个话语有关的话语数据的输入来预测单个话语的一类对话,其中对话类别通知各种老虎机灌装机学习模型;实现基于话语数据的输入,实现针对话语内的多个插槽中的每一个的时隙分类标签的插槽填充机学习模型;实现一个插槽关系提取机学习模型,其预测话语两个或多个不同斜槽之间的语义关系分类;基于单个话语的两个或多个不同槽的不同配对之间的语义关系分类,产生对单个话语的响应或执行动作的响应。

著录项

  • 公开/公告号US10970493B1

    专利类型

  • 公开/公告日2021-04-06

    原文格式PDF

  • 申请/专利权人 CLINC INC.;

    申请/专利号US202017014986

  • 申请日2020-09-08

  • 分类号G06F40;G06F40/35;G06N3/04;G06N20;

  • 国家 US

  • 入库时间 2022-08-24 18:04:46

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