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Deep Learning for Dialogue Systems

机译:对话系统深入学习

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摘要

Goal-oriented spoken dialogue systems have been the most prominent component in todays virtual personal assistants, which allow users to speak naturally in order to finish tasks more efficiently. The advancement of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge of the prior work and the recent state-of-the-art work. Therefore, this tutorial is designed to focus on an overview of dialogue system development while describing most recent research for building dialogue systems, and summarizing the challenges, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems.
机译:面向目标的口头对话系统是当今虚拟个人助理中最突出的组件,允许用户自然地说话,以便更有效地完成任务。深度学习技术的进步最近提高了神经模型对对话建模的应用。然而,应用用于建立强大和可扩展性对话系统的深度学习技术仍然是一个具有挑战性的任务和开放研究区域,因为它需要更深入地了解经典管道以及对现有工作的详细知识和最近的状态 - 艺术品。因此,本教程旨在专注于对话系统开发的概述,同时描述为建立对话系统的最新研究以及总结挑战,以便允许研究人员研究最先进的对话的潜在改进系统。

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