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Measuring Non-cooperation in Dialogue

机译:衡量对话中的不合作

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摘要

This paper introduces a novel method for measuring non-cooperation in dialogue. The key idea is that linguistic non-cooperation can be measured in terms of the extent to which dialogue participants deviate from conventions regarding the proper introduction and discharging of conversational obligations (e.g., the obligation to respond to a question). Previous work on non-cooperation has focused mainly on non-linguistic task-related non-cooperation or modelled non-cooperation in terms of special rules describing non-cooperative behaviours. In contrast, we start from rules for normal/correct dialogue behaviour - i.e., a dialogue game - which in principle can be derived from a corpus of cooperative dialogues, and provide a quantitative measure for the degree to which participants comply with these rules. We evaluated the model on a corpus of political interviews, with encouraging results. The model predicts accurately the degree of cooperation for one of the two dialogue game roles (interviewer) and also the relative cooperation for both roles (i.e., which interlocutor in the conversation was most cooperative). Being able to measure cooperation has applications in many areas from the analysis - manual, semi and fully automatic - of natural language interactions to human-like virtual personal assistants, tutoring agents, sophisticated dialogue systems, and role-playing virtual humans.
机译:本文介绍了一种测量对话中不合作的新方法。关键思想是,可以根据对话参与者在适当引入和履行对话义务(例如回答问题的义务)方面偏离惯例的程度来衡量语言上的不合作。根据描述非合作行为的特殊规则,先前关于非合作的工作主要集中在与语言无关的任务相关的非合作或模型化的非合作。相反,我们从正常/正确对话行为的规则(即对话游戏)开始,该规则原则上可以从合作对话的语料库中得出,并为参与者遵守这些规则的程度提供了定量的度量。我们根据一系列政治访谈对模型进行了评估,并得出了令人鼓舞的结果。该模型准确地预测了两个对话游戏角色(采访者)之一的合作程度,以及两个角色的相对合作(即对话中哪个对话者最合作)。能够衡量合作在从自然语言交互的分析(手动,半自动和全自动)到类似于人的虚拟个人助理,辅导员,复杂的对话系统以及扮演角色的虚拟人的许多领域中都有应用。

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