Evaluating Referring Form Selection Models in Partially-Known Environments
Evaluating Referring Form Selection Models in Partially-Known Environments
复制标题
评估部分已知环境中的参考表单选择模型
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
T. Williams
中科院分区：
文献类型：
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作者：
Zhao Han;Polina Rygina;T. Williams
为了让机器人等自主代理有效地与人类通信，它们必须能够在情景上下文中引用不同的实体。为了实现这一目标，研究者们最近尝试建立基于认知状态的指称形式选择的模型(由给定等级提供信息)，并显示出80%以上的准确率。然而，我们认为任务环境缺乏生态有效性，因为它们使用了少量不断激活并容易唯一识别的对象。因此，我们提出了一个新的建筑-建造任务，我们相信它已经增加了生态有效性。然后，我们展示了在这个新的任务环境中收集的数据上，训练认知状态通知参照形式选择模型如何产生与以前工作中发现的显著不同的结果，为未来的工作提供关键的见解和方向。
For autonomous agents such as robots to effectively communicate with humans, they must be able to refer to different entities in situated contexts. In service of this goal, researchers have recently attempted to model the selection of referring forms on the basis of cognitive status (informed by Givenness Hierarchy), and have shown promising results with over 80% accuracy. However, we argue that the task environments lack ecological validity, due to their use of a small number of objects that are constantly activated and easily uniquely identifiable. Accordingly, we present a novel building-construction task that we believe has increased ecological validity. We then show how training cognitive status informed referring form selection models on data collected within this novel task environment yields substantially different results from those found in previous work, providing key insights and directions for future work.
DOI：
10.7551/mitpress/9082.001.0001
发表时间：
2016-04
期刊：
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影响因子：
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作者：
Kees van Deemter
通讯作者：
Kees van Deemter
DOI：
10.1145/3434074.3447199
发表时间：
2021
期刊：
ACM/IEEE International Conference on Human-Robot Interaction
影响因子：
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作者：
Stogsdill, Adam;Clark, Grace;Ranucci, Aly;Phung, Thao;Williams, Tom
通讯作者：
Williams, Tom