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            <title>Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue</title>
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            <namePart type="given">Haizhou</namePart>
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            <namePart type="given">Gina-Anne</namePart>
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    <abstract>here is increasing interest in modeling style choices in dialog, for example for enabling dialog systems to adapt to their users. It is commonly assumed that each user has his or her own stable characteristics, but for interaction style the truth of this assumption has not been well examined. I investigated using a vector-space model of interaction styles, derived from the Switchboard corpus of telephone conversations and a broad set of prosodic-behavior features. While most individuals exhibited interaction style tendencies, these were generally far from stable, with a predictive model based on individual tendencies outperforming a speaker-independent model by only 3.6%. The tendencies were somewhat stronger for some speakers, including generally males, and for some dimensions of variation.</abstract>
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%0 Conference Proceedings
%T Individual Interaction Styles: Evidence from a Spoken Chat Corpus
%A Ward, Nigel
%Y Li, Haizhou
%Y Levow, Gina-Anne
%Y Yu, Zhou
%Y Gupta, Chitralekha
%Y Sisman, Berrak
%Y Cai, Siqi
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Wu, Yan
%Y Li, Junyi Jessy
%S Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2021
%8 July
%I Association for Computational Linguistics
%C Singapore and Online
%F ward-2021-individual
%X here is increasing interest in modeling style choices in dialog, for example for enabling dialog systems to adapt to their users. It is commonly assumed that each user has his or her own stable characteristics, but for interaction style the truth of this assumption has not been well examined. I investigated using a vector-space model of interaction styles, derived from the Switchboard corpus of telephone conversations and a broad set of prosodic-behavior features. While most individuals exhibited interaction style tendencies, these were generally far from stable, with a predictive model based on individual tendencies outperforming a speaker-independent model by only 3.6%. The tendencies were somewhat stronger for some speakers, including generally males, and for some dimensions of variation.
%R 10.18653/v1/2021.sigdial-1.4
%U https://aclanthology.org/2021.sigdial-1.4/
%U https://doi.org/10.18653/v1/2021.sigdial-1.4
%P 27-31

Markdown (Informal)

[Individual Interaction Styles: Evidence from a Spoken Chat Corpus](https://aclanthology.org/2021.sigdial-1.4/) (Ward, SIGDIAL 2021)

• Individual Interaction Styles: Evidence from a Spoken Chat Corpus (Ward, SIGDIAL 2021)

ACL

• Nigel Ward. 2021. Individual Interaction Styles: Evidence from a Spoken Chat Corpus. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 27–31, Singapore and Online. Association for Computational Linguistics.