An ECA expressing appreciations

Campano, Sabrina; Langlet, Caroline; Glas, Nadine; Clavel, Chloé; Pélachaud, Catherine

doi:10.1109/acii.2015.7344691

Cited by 13 publications

(15 citation statements)

References 18 publications

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“…Several studies aim at providing virtual agents with the ability to verbally align with the user in order to improve credibility, naturalness, and also to foster user engagement (Clavel et al, 2016). It involves high-level alignment such as politeness (De Jong et al, 2008) or aligning on appreciations (Campano et al, 2015). Work on convergence in the spoken dialogue system community has mainly focused on lexical entrainment, i.e.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Measures to Characterise Verbal Alignment in Human-Agent Interaction

Duplessis¹,

Clavel²,

Landragin³

2017

Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

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This work aims at characterising verbal alignment processes for improving virtual agent communicative capabilities. We propose computationally inexpensive measures of verbal alignment based on expression repetition in dyadic textual dialogues. Using these measures, we present a contrastive study between Human-Human and Human-Agent dialogues on a negotiation task. We exhibit quantitative differences in the strength and orientation of verbal alignment showing the ability of our approach to characterise important aspects of verbal alignment.

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Section: Related Workmentioning

confidence: 99%

“…It involves high-level alignment such as politeness (De Jong et al, 2008) or aligning on appreciations (Campano et al, 2015). Work on convergence in the spoken dialogue system community has mainly focused on lexical entrainment, i.e.…”

Section: Related Workmentioning

confidence: 99%

Automatic Measures to Characterise Verbal Alignment in Human-Agent Interaction

Duplessis¹,

Clavel²,

Landragin³

2017

Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

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“…Linguistic mimicry is a device that could also be used by artificial agents to emulate empathic interactions with humans. The idea of agents adapting or aligning to an interlocutor's linguistic style for improving user engagement is not new [De Jong et al 2008;Campano et al 2015]. In fact, the state-of-the-art in human-computer interaction (HCI) shows that people tend to align aspects of their language with their conversational partners, both humans and computers/agents [Cowan et al 2015], and even very strong default preferences of the user may be influenced by an interlocutor's linguistic choices.…”

Section: Discussionmentioning

confidence: 99%

“…Here, the researchers described a model for dynamically adapting the linguistic style of a virtual agent to the level of politeness formality detected in the user's own utterances. Another approach to linguistic alignment used by an Embodied Conversational Agent (ECA) in response to the user's utterances was presented in [Campano et al 2015]. This work introduced a computational model of other-repetitions (ORs), conveying emotional stances.…”

Section: Mimicry and Linguistic Stylementioning

confidence: 99%

Show Me You Care

Otterbacher

Ang

Litvak

et al. 2017

ACM Trans. Internet Technol.

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“…The goal of our current work is to model W&C dimensions in order to obtain an engaging ECA, by following the idea that a more engaging agent is likely to form a positive impression and be accepted by the user, thus promoting further interactions (Bergmann et al, 2012 ; Cafaro et al, 2017 ). Several authors attempted to design engaging virtual agents, by focusing on the use of feedback and backchannels (Truong et al, 2010 ), by adopting politeness strategies (Glas and Pelachaud, 2015b ), or by investigating the role of verbal alignment for improving user's engagement (Campano et al, 2015 ). Other studies focused on how to improve user's engagement by adapting social agents (mainly robots) behaviors, using reinforcement learning (RL) methods.…”

Section: Related Workmentioning

confidence: 99%

Managing an Agent's Self-Presentational Strategies During an Interaction

Biancardi¹,

Mancini

Lerner

et al. 2019

Front. Robot. AI

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In this paper we present a computational model for managing the impressions of warmth and competence (the two fundamental dimensions of social cognition) of an Embodied Conversational Agent (ECA) while interacting with a human. The ECA can choose among four different self-presentational strategies eliciting different impressions of warmth and/or competence in the user, through its verbal and non-verbal behavior. The choice of the non-verbal behaviors displayed by the ECA relies on our previous studies. In our first study, we annotated videos of human-human natural interactions of an expert on a given topic talking to a novice, in order to find associations between the warmth and competence elicited by the expert's non-verbal behaviors (such as type of gestures, arms rest poses, smiling). In a second study, we investigated whether the most relevant non-verbal cues found in the previous study were perceived in the same way when displayed by an ECA. The computational learning model presented in this paper aims to learn in real-time the best strategy (i.e., the degree of warmth and/or competence to display) for the ECA, that is, the one which maximizes user's engagement during the interaction. We also present an evaluation study, aiming to investigate our model in a real context. In the experimental scenario, the ECA plays the role of a museum guide introducing an exposition about video games. We collected data from 75 visitors of a science museum. The ECA was displayed in human dimension on a big screen in front of the participant, with a Kinect on the top. During the interaction, the ECA could adopt one of 4 self-presentational strategies during the whole interaction, or it could select one strategy randomly for each speaking turn, or it could use a reinforcement learning algorithm to choose the strategy having the highest reward (i.e., user's engagement) after each speaking turn.

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An ECA expressing appreciations

Cited by 13 publications

References 18 publications

Automatic Measures to Characterise Verbal Alignment in Human-Agent Interaction

Automatic Measures to Characterise Verbal Alignment in Human-Agent Interaction

Show Me You Care

Managing an Agent's Self-Presentational Strategies During an Interaction

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