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Towards alignment strategies in human-agent interactions based on measures of lexical repetitions

机译：基于词汇重复措施的人代代理相互作用对准策略

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摘要

Alignment of communicative behaviour is an important feature of Human-Human interaction that directly affects the collaboration and the social connection of conversational partners. With the aim of improving the communicative abilities of a virtual agent, and in particular its strategies related to (lexical) verbal alignment, this article focuses on the alignment of linguistic productions of dialogue participants in task-oriented dialogues. We propose a new framework to quantify both the lexical alignment and the self-repetition behaviours of dialogue participants from dyadic dialogue transcripts. The framework involves easily computable measures based on repetition of lexical patterns automatically extracted via a sequential pattern mining approach. These measures allow the characterisation of the nature of these processes by addressing various informative aspects such as their variety, complexity, and strength. This framework is implemented in the freely available and open-source software dialign. Using these measures, we present a contrastive study between Human-Human and Human-Agent dialogues on various corpora that reveals major differences in the lexical alignment and self-repetition behaviours. Lastly, we address the challenge of integrating lexical alignment capabilities in artificial agents. To this end, we describe guidelines and we discuss the integration of the proposed framework in a real-time dialogue system.
机译：交际行为的对准是人类互动的一个重要特征，即直接影响对话伙伴的合作和社会联系。旨在提高虚拟代理人的交流能力，特别是其与（词汇）言语对齐有关的策略，本文侧重于对话与对话参与者在面向任务对话中的一致性的对齐。我们提出了一个新的框架，以量化词汇比赛和对话参与者的自我重复行为从二元对话转录物中进行对话。该框架涉及基于通过顺序模式挖掘方法自动提取的词汇模式的重复的可易于计算的措施。这些措施允许通过解决各种信息性方面的各种信息，以及它们的种类，复杂性和力量来表征这些过程的性质。此框架是在自由和开源的软件障碍物中实现的。使用这些措施，我们对人类人体和人类代理对话的对比研究，在各种数集上揭示了词汇对齐和自我重复行为的重大差异。最后，我们解决了整合人工代理中的词汇对准能力的挑战。为此，我们描述了指导方针，我们讨论了在实时对话系统中拟议框架的整合。

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