Investigating Proactivity in Task-Oriented Dialogues

Vevake Balaraman, B. Magnini
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引用次数: 2

Abstract

Proactivity (i.e., the capacity to provide useful information even when not explicitly required) is a fundamental characteristic of human dialogues. Although current task-oriented dialogue systems are good at providing information explicitly requested by the user, they are poor in exhibiting proactivity, which is typical in humanhuman interactions. In this study, we investigate the presence of proactive behaviours in several available dialogue collections, both human-human and humanmachine and show how the data acquisition decision affects the proactive behaviour present in the dataset. We adopt a two-step approach to semi-automatically detect proactive situations in the datasets, where proactivity is not annotated, and show that the dialogues collected with approaches that provide more freedom to the agent/user, exhibit high proactivity.
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任务导向对话中的主动性研究
主动(即即使在没有明确要求的情况下提供有用信息的能力)是人类对话的基本特征。虽然当前面向任务的对话系统在提供用户明确要求的信息方面表现良好,但在展示人类互动中典型的主动性方面表现较差。在本研究中,我们调查了几个可用的对话集合中主动行为的存在,包括人机和人机,并展示了数据采集决策如何影响数据集中存在的主动行为。我们采用两步方法半自动检测数据集中的主动情况,其中主动性未被注释,并表明使用为代理/用户提供更多自由的方法收集的对话显示出高主动性。
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