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Resonance as an applied predictor of cross-cultural diversity and a resource for AI conversational interfaces

Research output: Contribution to journalJournal articlepeer-review

Forthcoming
<mark>Journal publication date</mark>2/02/2021
<mark>Journal</mark>Applied Linguistics
Publication StatusAccepted/In press
<mark>Original language</mark>English

Abstract

In Dialogic syntax (cf. Du Bois 2014; Author et al. 2018), naturalistic interaction is inherently grounded in resonance, viz. the catalytic activation of affinities across turns (Du Bois & Giora 2014). Resonance occurs dynamically when interlocutors creatively co-construct utterances that are formally and phonetically similar to the utterance of a prior speaker. In this study we argue that such similarity can inform the machine-learning prediction of linguistic and cross-cultural diversity. We compared two sets of 1000 exchanges involving (dis)-agreement from the two balanced Callhome corpora of naturalistic interaction in Mandarin Chinese and American English. We found a correlation of overt use of pragmatic markers with resonance, indicating that priming does not occur as an exclusively implicit mechanism (as it is commonly held in the experimental literature e.g. Bock 1986; Bock et al. 2007), but naturalistically underpins dialogic engagement and cooperation among interactants. We fitted a mixed effects linear regression and hierarchical clustering model to show that resonance occurs formally and functionally in different ways from one language to another. The applied results of this study can lead to a novel turn in AI research of conversational interfaces (McTear et al. 2016; Klopfenstein et al. 2017), as they reveal the fundamental role played cross-linguistically by resonance as a form of engagement of human-to-human interaction, and the importance to address this mechanism in machine-to-human communication.