Exploring the Applicability of Elaborateness and Indirectness in Dialogue Management

Pragst, Louisa; Minker, Wolfgang; Ultes, Stefan

doi:10.1007/978-3-319-92108-2_20

Cited by 7 publications

(7 citation statements)

References 10 publications

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“…Meanwhile, different descriptions of the same data may lead to different outcomes. For example, Pragst et al [13] demonstrated that different levels of elaborateness and indirectness can influence users' perceptions of the information provided.…”

Section: A Technology-based Behavior Change Promotionmentioning

confidence: 99%

“…One adaption approach is keeping the propositional content of a system action the same and changing only the way it is presented, i.e., the communication style. Pragst et al [13] investigated the applicability of elaborateness and indirectness as possibilities for adaptation in human-computer interaction. They found that these dimensions influence the user's perception of dialogue and are therefore valuable candidates for adaptive dialogue management.…”

Section: B Adaptive Communication Stylesmentioning

confidence: 99%

“…Meanwhile, different descriptions of the same data may lead to different outcomes. According to Pragst et al [13], the elaborateness and indirectness of content can influence users' perceptions of the information provided. Miehle et al [14], [15] demonstrated that the elaborateness and indirectness of content influence user satisfaction in a spoken dialogue system.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Impacts of Elaborateness and Indirectness in a Behavior Change Support System

et al. 2021

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Numerous technologies exist for promoting a healthier lifestyle. These technologies collectively referred to as "Behavior Change Support Systems". However, the majority of existing apps use quantitative data representation. Since it is difficult to understand the meaning behind quantitative data, this approach has been suggested to lower users' motivation and fail to promote behavior change. Therefore, an interpretation of quantitative data needs to be provided as a supplement. However, different descriptions of the same data may lead to different outcomes. In this paper, we explore the impact of different communication styles for interpretations of quantitative data on behavior change by developing and evaluating Walkeeper -a webbased app that provides interpretations of the users' daily step counts using different levels of elaborateness and indirectness with the aim of promoting walking. Through the quantitative analysis and results of a user study, we contribute new knowledge on designing such interpretations for quantitative data.INDEX TERMS behavior change, communication styles, physical activity.

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Section: A Technology-based Behavior Change Promotionmentioning

confidence: 99%

Section: B Adaptive Communication Stylesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the Impacts of Elaborateness and Indirectness in a Behavior Change Support System

et al. 2021

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“…These works compared lab experiments with crowdsourced ones -in the scenario of spoken dialogue systems -showing that the results in the former (direct interaction with the system) are comparable with the results in the latter (third party users reading transcriptions). Similarly, in (Pragst et al, 2017), the authors focus on a WoZ evaluation of the interaction strategies of an embodied conversational agents. Users were presented with the video of an embodied conversational agent interacting with a human user (the agent was guided by a Wizard and the user was an instructed actor).…”

Section: Related Workmentioning

confidence: 99%

A Methodology for Evaluating Interaction Strategies of Task-Oriented Conversational Agents

Guerini

Falcone²,

Magnini

2018

Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI

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In task-oriented conversational agents, more attention has been usually devoted to assessing task effectiveness, rather than to how the task is achieved. However, conversational agents are moving towards more complex and human-like interaction capabilities (e.g. the ability to use a formal/informal register, to show an empathetic behavior), for which standard evaluation methodologies may not suffice. In this paper, we provide a novel methodology to assess-in a completely controlled way-the impact on the quality of experience of agent's interaction strategies. The methodology is based on a within subject design, where two slightly different transcripts of the same interaction with a conversational agent are presented to the user. Through a series of pilot experiments we prove that this methodology allows fast and cheap experimentation/evaluation, focusing on aspects that are overlooked by current methods.

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“…Studies could show that under specific circumstances indirectness is preferred not only from human conversation partners, but also in human-computer interaction (e.g. (Miehle et al, 2016;Pragst et al, 2017)). Therefore, dialogue systems that can adjust the level of directness in their output to the user and their circumstances should be able to provide an improved user experience.…”

Section: Introductionmentioning

confidence: 99%

Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks

Pragst

Ultes

2018

Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

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In cooperative dialogues, identifying the intent of ones conversation partner and acting accordingly is of great importance. While this endeavour is facilitated by phrasing intentions as directly as possible, we can observe in human-human communication that a number of factors such as cultural norms and politeness may result in expressing one's intent indirectly. Therefore, in human-computer communication we have to anticipate the possibility of users being indirect and be prepared to interpret their actual meaning. Furthermore, a dialogue system should be able to conform to human expectations by adjusting the degree of directness it uses to improve the user experience. To reach those goals, we propose an approach to differentiate between direct and indirect utterances and find utterances of the opposite characteristic that express the same intent. In this endeavour, we employ dialogue vector models and recurrent neural networks.

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Exploring the Applicability of Elaborateness and Indirectness in Dialogue Management

Cited by 7 publications

References 10 publications

Exploring the Impacts of Elaborateness and Indirectness in a Behavior Change Support System

Exploring the Impacts of Elaborateness and Indirectness in a Behavior Change Support System

A Methodology for Evaluating Interaction Strategies of Task-Oriented Conversational Agents

Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks

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