Towards alignment strategies in human-agent interactions based on measures of lexical repetitions.
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...
| Publicado en: | Language Resources & Evaluation Vol. 55; no. 2; pp. 353 - 389 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=150471579&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 150471579 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2021 vid: 55 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 150471579 10.1007/s10579-021-09532-w ppf: 353 ppct: 36 formats: fmt: @attributes: type: P size: 544KB tig: atl: Towards alignment strategies in human-agent interactions based on measures of lexical repetitions. aug: au: Dubuisson Duplessis, Guillaume Langlet, Caroline Clavel, Chloé Landragin, Frédéric affil: CNRS, ISIR, UPMC Université Paris 6, Sorbonne Universités, Paris, France ISIR, Sorbonne Universités, Paris, France LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France Lattice, CNRS, ENS, Université Paris 3, PSL Research University, Paris, France su: Sequential pattern mining Communicative competence sug: subj: Sequential pattern mining Communicative competence keyword: Human–agent interaction Interaction strategies Verbal alignment ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2021. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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