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首页> 外文期刊>Philosophical Transactions of the Royal Society of London, Series B. Biological Sciences >Natural language generation for social robotics: opportunities and challenges
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Natural language generation for social robotics: opportunities and challenges

机译：社会机器人的自然语言生成：机遇和挑战

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摘要

In the increasingly popular and diverse research area of social robotics, the primary goal is to develop robot agents that exhibit socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face social conversation is fluent, flexible linguistic interaction; face-to-face dialogue is both the basic form of human communication and the richest and most flexible, combining unrestricted verbal expression with meaningful non-verbal acts such as gestures and facial displays, along with instantaneous, continuous collaboration between the speaker and the listener. In practice, however, most developers of social robots tend not to use the full possibilities of the unrestricted verbal expression afforded by face-to-face conversation; instead, they generally tend to employ relatively simplistic processes for choosing the words for their robots to say. This contrasts with the work carried out Natural Language Generation (NLG), the field of computational linguistics devoted to the automated production of high-quality linguistic content; while this research area is also an active one, in general most effort in NLG is focused on producing high-quality written text. This article summarizes the state of the art in the two individual research areas of social robotics and natural language generation. It then discusses the reasons why so few current social robots make use of more sophisticated generation techniques. Finally, an approach is proposed to bringing some aspects of NLG into social robotics, concentrating on techniques and tools that are most appropriate to the needs of socially interactive robots.
机译：在越来越流行的社会机器人的研究领域，主要目标是开发在与人类合作伙伴的面对面背景下互动的同时开发具有社会智能行为的机器人代理。面对面社交谈话的一个重要方面是流利，灵活的语言互动;面对面的对话是人类通信的基本形式和最富有，最灵活，与诸如手势和面部显示器等有意义的非言语行为相结合的无限制的口头表达，以及讲话者和听众之间的瞬时连续合作。然而，在实践中，大多数社会机器人的开发商都不会利用面对面对话所提供的不受限制的口头表达的完整可能性;相反，它们通常倾向于采用相对简单的过程来为其机器人选择单词。这种对比与工作进行了自然语言生成（NLG），致力于自动化高质量的语言内容的计算语言学领域;虽然这一研究区也是一个活跃的，但通常，NLG的大多数努力都集中在生产高质量的书面文本。本文总结了社会机器人和自然语言生成的两个研究领域的艺术状态。然后讨论了这么少的社会机器人利用更复杂的发电技术的原因。最后，提出了一种方法，以将NLG的某些方面带入社会机器人，专注于最适合社会交互式机器人需求的技术和工具。

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