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Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving / David R. Large, Leigh Clark, Annie Quandt, Gary Burnett, Lee Skrypchuk

Applied Ergonomics, Volume: 63, Pages: 53 - 61

Swansea University Author: Leigh Clark

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Abstract

Given the proliferation of ‘intelligent’ and ‘socially-aware’ digital assistants embodying everyday mobile technology – and the undeniable logic that utilising voice-activated controls and interfaces in cars reduces the visual and manual distraction of interacting with in-vehicle devices – it appear...

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Published in: Applied Ergonomics
ISSN: 0003-6870
Published: Elsevier BV 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa52486
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From a design perspective, defining the language and interaction style that a digital driving assistant should adopt is contingent on the role that they play within the social fabric and context in which they are situated. We therefore conducted a qualitative, Wizard-of-Oz study to explore how drivers might interact linguistically with a natural language digital driving assistant. Twenty-five participants drove for 10 min in a medium-fidelity driving simulator while interacting with a state-of-the-art, high-functioning, conversational digital driving assistant. All exchanges were transcribed and analysed using recognised linguistic techniques, such as discourse and conversation analysis, normally reserved for interpersonal investigation. Language usage patterns demonstrate that interactions with the digital assistant were fundamentally social in nature, with participants affording the assistant equal social status and high-level cognitive processing capability. For example, participants were polite, actively controlled turn-taking during the conversation, and used back-channelling, fillers and hesitation, as they might in human communication. Furthermore, participants expected the digital assistant to understand and process complex requests mitigated with hedging words and expressions, and peppered with vague language and deictic references requiring shared contextual information and mutual understanding. Findings are presented in six themes which emerged during the analysis &#x2013; formulating responses; turn-taking; back-channelling, fillers and hesitation; vague language; mitigating requests and politeness and praise. 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spelling 2021-01-28T13:23:18.4383352 v2 52486 2019-10-17 Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving 004ef41b90854a57a498549a462f13a0 0000-0002-9237-1057 Leigh Clark Leigh Clark true false 2019-10-17 SCS Given the proliferation of ‘intelligent’ and ‘socially-aware’ digital assistants embodying everyday mobile technology – and the undeniable logic that utilising voice-activated controls and interfaces in cars reduces the visual and manual distraction of interacting with in-vehicle devices – it appears inevitable that next generation vehicles will be embodied by digital assistants and utilise spoken language as a method of interaction. From a design perspective, defining the language and interaction style that a digital driving assistant should adopt is contingent on the role that they play within the social fabric and context in which they are situated. We therefore conducted a qualitative, Wizard-of-Oz study to explore how drivers might interact linguistically with a natural language digital driving assistant. Twenty-five participants drove for 10 min in a medium-fidelity driving simulator while interacting with a state-of-the-art, high-functioning, conversational digital driving assistant. All exchanges were transcribed and analysed using recognised linguistic techniques, such as discourse and conversation analysis, normally reserved for interpersonal investigation. Language usage patterns demonstrate that interactions with the digital assistant were fundamentally social in nature, with participants affording the assistant equal social status and high-level cognitive processing capability. For example, participants were polite, actively controlled turn-taking during the conversation, and used back-channelling, fillers and hesitation, as they might in human communication. Furthermore, participants expected the digital assistant to understand and process complex requests mitigated with hedging words and expressions, and peppered with vague language and deictic references requiring shared contextual information and mutual understanding. Findings are presented in six themes which emerged during the analysis – formulating responses; turn-taking; back-channelling, fillers and hesitation; vague language; mitigating requests and politeness and praise. The results can be used to inform the design of future in-vehicle natural language systems, in particular to help manage the tension between designing for an engaging dialogue (important for technology acceptance) and designing for an effective dialogue (important to minimise distraction in a driving context). Journal Article Applied Ergonomics 63 53 61 Elsevier BV 0003-6870 Natural language interface, Digital assistant, Social AIs, DrivingSimulation, Wizard-of-Oz 30 9 2017 2017-09-30 10.1016/j.apergo.2017.04.003 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-01-28T13:23:18.4383352 2019-10-17T13:51:44.7181723 College of Science Computer Science David R. Large 1 Leigh Clark 0000-0002-9237-1057 2 Annie Quandt 3 Gary Burnett 4 Lee Skrypchuk 5 52486__15657__f1d3e179caed43b7af869df4ca075297.pdf JERG-D-16-00728R2.20Large.pdf 2019-10-17T14:05:36.2470000 Output 293243 application/pdf Accepted Manuscript true 2019-10-17T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-commercial No Derivatives license (CC-BY-NC-ND). true eng https://creativecommons.org/licenses/by-nc-nd/2.5/
title Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving
spellingShingle Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving
Leigh, Clark
title_short Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving
title_full Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving
title_fullStr Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving
title_full_unstemmed Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving
title_sort Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving
author_id_str_mv 004ef41b90854a57a498549a462f13a0
author_id_fullname_str_mv 004ef41b90854a57a498549a462f13a0_***_Leigh, Clark
author Leigh, Clark
author2 David R. Large
Leigh Clark
Annie Quandt
Gary Burnett
Lee Skrypchuk
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container_start_page 53
publishDate 2017
institution Swansea University
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doi_str_mv 10.1016/j.apergo.2017.04.003
publisher Elsevier BV
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description Given the proliferation of ‘intelligent’ and ‘socially-aware’ digital assistants embodying everyday mobile technology – and the undeniable logic that utilising voice-activated controls and interfaces in cars reduces the visual and manual distraction of interacting with in-vehicle devices – it appears inevitable that next generation vehicles will be embodied by digital assistants and utilise spoken language as a method of interaction. From a design perspective, defining the language and interaction style that a digital driving assistant should adopt is contingent on the role that they play within the social fabric and context in which they are situated. We therefore conducted a qualitative, Wizard-of-Oz study to explore how drivers might interact linguistically with a natural language digital driving assistant. Twenty-five participants drove for 10 min in a medium-fidelity driving simulator while interacting with a state-of-the-art, high-functioning, conversational digital driving assistant. All exchanges were transcribed and analysed using recognised linguistic techniques, such as discourse and conversation analysis, normally reserved for interpersonal investigation. Language usage patterns demonstrate that interactions with the digital assistant were fundamentally social in nature, with participants affording the assistant equal social status and high-level cognitive processing capability. For example, participants were polite, actively controlled turn-taking during the conversation, and used back-channelling, fillers and hesitation, as they might in human communication. Furthermore, participants expected the digital assistant to understand and process complex requests mitigated with hedging words and expressions, and peppered with vague language and deictic references requiring shared contextual information and mutual understanding. Findings are presented in six themes which emerged during the analysis – formulating responses; turn-taking; back-channelling, fillers and hesitation; vague language; mitigating requests and politeness and praise. The results can be used to inform the design of future in-vehicle natural language systems, in particular to help manage the tension between designing for an engaging dialogue (important for technology acceptance) and designing for an effective dialogue (important to minimise distraction in a driving context).
published_date 2017-09-30T04:08:18Z
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