Givenness Hierarchy Informed Optimal Document Planning for Situated Human-Robot Interaction
Givenness Hierarchy Informed Optimal Document Planning for Situated Human-Robot Interaction
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Givenness 层次结构为情景人机交互提供最佳文档规划
DOI：
10.1109/iros47612.2022.9981811
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Neil T. Dantam
中科院分区：
文献类型：
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作者：
Kevin Spevak;Zhao Han;Tom Williams;Neil T. Dantam
在协作任务中使用自然语言的机器人必须引用其环境中的对象。最近的研究表明，在生成适当的指称形式的语言学理论的给予等级（GH）的效用。但在引用表达生成之前，协作机器人必须确定一系列话语的内容和结构，这在自然语言生成社区中被称为文档规划。这个问题提出了额外的挑战，机器人在情境中，所描述的对象改变物理和他们的对话者的头脑。在这项工作中，我们考虑机器人如何“提前思考”它们必须引用的对象以及如何引用它们，对对象引用进行排序，以形成一个连贯的，易于遵循的链。具体来说，我们利用GH，使机器人计划他们的话语的方式，保持对象在一个高的认知状态，这使得使用简洁的，照应的指涉形式。我们将这些语言见解编码为规划背景下的混合整数规划，制定约束条件，以简洁有效地捕捉GH理论的认知特性。我们证明，这个GH知情的计划者生成的话语序列具有高intermittial连贯性，我们认为应该使更有效和自然的人机对话。
Robots that use natural language in collaborative tasks must refer to objects in their environment. Recent work has shown the utility of the linguistic theory of the Givenness Hierarchy (GH) in generating appropriate referring forms. But before referring expression generation, collaborative robots must determine the content and structure of a sequence of utterances, a task known as document planning in the natural language generation community. This problem presents additional challenges for robots in situated contexts, where described objects change both physically and in the minds of their interlocutors. In this work, we consider how robots can “think ahead” about the objects they must refer to and how to refer to them, sequencing object references to form a coherent, easy to follow chain. Specifically, we leverage GH to enable robots to plan their utterances in a way that keeps objects at a high cognitive status, which enables use of concise, anaphoric referring forms. We encode these linguistic insights as a mixed integer program within a planning context, formulating constraints to concisely and efficiently capture GH-theoretic cognitive properties. We demonstrate that this GH-informed planner generates sequences of utterances with high intersentential coherence, which we argue should enable substantially more efficient and natural human-robot dialogue.