A computational model for automatic generation of domain-specific dialogues using machine learning

Andrés Vázquez, David Pinto, D. V. Ayala
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Abstract

Automatic generation of dialogues is a very important component for the human-robot interaction task ; the dialogues generated must guarantee a coherent conversation between human beings and robots. The aim is that the interaction is as natural and effective as possible, considering aspects such as: age, gender, socio-cultural level, socio-economic level, and so on. This research report presents an overview of a doctoral research work that is intended to be executed during the following three years. We motivate the necessity of researching in automatic generation and evaluation of dialogues in the framework of human-robot interaction, presenting the related work reported in literature, the research objectives and the methodology we are interested in develop. In general, we propose to employ machine learning techniques in a restricted domain of knowledge for generating the human-robot dialogues.
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使用机器学习自动生成特定领域对话的计算模型
对话的自动生成是人机交互任务的重要组成部分;生成的对话必须保证人与机器人之间的连贯对话。其目的是在考虑到年龄、性别、社会文化水平、社会经济水平等方面的情况下,使互动尽可能自然和有效。这份研究报告概述了一项博士研究工作,该工作将在接下来的三年内执行。我们提出了在人机交互框架下研究对话自动生成和评估的必要性,介绍了文献报道的相关工作,研究目标和我们感兴趣的研究方法。一般来说,我们建议在有限的知识领域中使用机器学习技术来生成人机对话。
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