Towards Personalization of Spoken Dialogue System Communication Strategies (bibtex)
by Gordon, Carla, Georgila, Kallirroi, Yanov, Volodymyr and Traum, David
Abstract:
This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
Reference:
Towards Personalization of Spoken Dialogue System Communication Strategies (Gordon, Carla, Georgila, Kallirroi, Yanov, Volodymyr and Traum, David), Chapter in Conversational Dialogue Systems for the Next Decade, Springer Singapore, volume 704, 2020.
Bibtex Entry:
@incollection{gordon_towards_2020,
	address = {Singapore},
	title = {Towards {Personalization} of {Spoken} {Dialogue} {System} {Communication} {Strategies}},
	volume = {704},
	isbn = {9789811583940 9789811583957},
	url = {http://link.springer.com/10.1007/978-981-15-8395-7_11},
	abstract = {This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.},
	booktitle = {Conversational {Dialogue} {Systems} for the {Next} {Decade}},
	publisher = {Springer Singapore},
	author = {Gordon, Carla and Georgila, Kallirroi and Yanov, Volodymyr and Traum, David},
	month = sep,
	year = {2020},
	keywords = {ARO-Coop, Virtual Humans},
	pages = {145--160}
}
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