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.

交往行为的统一是人与人互动的一个重要特征，它直接影响对话伙伴的协作和社交联系。为了提高虚拟代理的沟通能力，尤其是与（词汇）语言对齐有关的策略，本文着重于面向任务的对话中对话参与者的语言产品的对齐。我们提出了一个新的框架来量化二进对话记录的对话参与者的词汇对齐和自我重复行为。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而易于计算的度量。这些措施通过解决各种信息方面（例如多样性，复杂性和优势）来表征这些过程的性质。这个框架是在免费提供的开源软件dialign中实现的。使用这些度量，我们在各种语料库上的人与人对话与人与人对话之间进行了对比研究，揭示了词汇排列和自我重复行为方面的主要差异。最后，我们解决了在人工代理中集成词汇对齐功能的挑战。为此，我们描述了准则，并讨论了在实时对话系统中所提出框架的集成。使用这些度量，我们在各种语料库上的人与人对话与人与人对话之间进行了对比研究，揭示了词汇排列和自我重复行为方面的主要差异。最后，我们解决了在人工代理中集成词汇对齐功能的挑战。为此，我们描述了准则，并讨论了在实时对话系统中所提出框架的集成。使用这些度量，我们在各种语料库上的人与人对话与人与人对话之间进行了对比研究，揭示了词汇排列和自我重复行为方面的主要差异。最后，我们解决了在人工代理中集成词汇对齐功能的挑战。为此，我们描述了准则，并讨论了在实时对话系统中所提出框架的集成。