Spectral Analysis of Information Density in Dialogue Predicts
            Collaborative Task Performance

Xu, Yang; Reitter, David

doi:10.18653/v1/p17-1058

Cited by 7 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This has had particular importance in understanding the role of working memory in alignment and how it might contribute to facilitating alignment across linguistic levels (Reitter et al, 2011). Whether the turn-to-turn alignment results reported would hold across longer time scales is an open question, and intersects with a growing interest in understanding multiscale alignment in other behavioral domains (Abney et al, 2014;Xu & Reitter, 2017). We believe such analysis could be easily integrated with ALIGN procedures.…”

Section: Areas For Future Align Developmentmentioning

confidence: 98%

ALIGN: Analyzing linguistic interactions with generalizable techNiques—A Python library.

Duran¹,

Paxton²,

Fusaroli³

2019

Psychological Methods

View full text Add to dashboard Cite

Linguistic alignment (LA) is the tendency during a conversation to reuse each other’s linguistic expressions, including lexical, conceptual, or syntactic structures. LA is often argued to be a crucial driver in reciprocal understanding and interpersonal rapport, though its precise dynamics and effects are still controversial. One barrier to more systematic investigation of these effects lies in the diversity in the methods employed to analyze LA, which makes it difficult to integrate and compare results of individual studies. To overcome this issue, we have developed ALIGN (Analyzing Linguistic Interactions with Generalizable techNiques), an open-source Python package to measure LA in conversation (https://pypi.python.org/pypi/align) along with in-depth open-source tutorials hosted on ALIGN’s GitHub repository (https://github.com/nickduran/align-linguistic-alignment). Here, we first describe the challenges in the study of LA and outline how ALIGN can address them. We then demonstrate how our analytical protocol can be applied to theory-driven questions using a complex corpus of dialogue (the Devil’s Advocate corpus; Duran & Fusaroli, 2017). We close by identifying further challenges and point to future developments of the field.

show abstract

Section: Areas For Future Align Developmentmentioning

confidence: 98%

ALIGN: Analyzing linguistic interactions with generalizable techNiques—A Python library.

Duran¹,

Paxton²,

Fusaroli³

2019

Psychological Methods

View full text Add to dashboard Cite

show abstract

“…Moreover, we expect the type of repairs to be affected by the contexts, with a higher frequency of the more specific kinds of repair types in taskoriented conversations compared with affiliative conversations. Linguistic entrainment has shown more mixed results, with some studies arguing for a positive relation to performance (the more similar, the easier to coordinate, see Gries, 2005;Ireland & Henderson, 2014;Reitter & Moore, 2014;Slocombe et al, 2013) and others for a negative relation (the more similar, the less we contribute to each other's solutions, see Fusaroli et al, 2012;Tylén et al, 2020;Xu & Reitter, 2017). To refine our hypotheses, we explored two existing corpora of affiliative and task-oriented conversations (see Section 1 in the online supplemental materials).…”

Section: Part I: Conversationsmentioning

confidence: 99%

Quantifying the interplay of conversational devices in building mutual understanding.

Dideriksen¹,

Christiansen²,

Tylén³

et al. 2023

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

Humans readily engage in idle chat and heated discussions and negotiate tough joint decisions without ever having to think twice about how to keep the conversation grounded in mutual understanding. However, current attempts at identifying and assessing the conversational devices that make this possible are fragmented across disciplines and investigate single devices within single contexts. We present a comprehensive conceptual framework to investigate conversational devices, their relations, and how they adjust to contextual demands. In two corpus studies, we systematically test the role of three conversational devices: backchannels, repair, and linguistic entrainment. Contrasting affiliative and task-oriented conversations within participants, we find that conversational devices adaptively adjust to the increased need for precision in the latter: We show that low-precision devices such as backchannels are more frequent in affiliative conversations, whereas more costly but higher-precision mechanisms, such as specific repairs, are more frequent in task-oriented conversations. Further, task-oriented conversations involve higher complementarity of contributions in terms of the content and perspective: lower semantic entrainment and less frequent (but richer) lexical and syntactic entrainment. Finally, we show that the observed variations in the use of conversational devices are potentially adaptive: pairs of interlocutors that show stronger linguistic complementarity perform better across the two tasks. By combining motivated comparisons of several conversational contexts and theoretically informed computational analyses of empirical data the present work lays the foundations for a comprehensive conceptual framework for understanding the use of conversational devices in dialogue.

show abstract

“…There are conceptual arguments suggesting that alignment should facilitate performance ), yet studies that quantitatively assessed this relation have yielded mixed results. For instance, different forms of lexical, syntactic and semantic alignment have been both positively (Gries, 2005;Ireland & Henderson, 2014;Reitter & Moore, 2014;Slocombe et al, 2013;Tylén et al, 2016) and negatively (Fusaroli et al, 2012;Tylén et al, 2016;Xu & Reitter, 2017) associated with task performance.…”

Section: Study 3: Analyses Of Task Performancementioning

confidence: 99%

“…Given these differences in contextual demands, we expect the use of conversational grounding mechanisms to vary across contexts (Fusaroli et al, 2017;Healey, Purver, & Howes, 2014;Reitter & Moore, 2014). Further, in conversational contexts demanding a high degree of precision, we should expect variability in the use of grounding mechanisms to be associated with variability in performance (e.g., the more interlocutors align in a task-oriented task, the better they should perform on this task; Gries, 2005;Reitter & Moore, 2006, 2014Slocombe et al, 2013), however, see also (Fusaroli et al, 2012;Tylén, Fusaroli, Smith, & Arnoldi, 2016;Xu & Reitter, 2017).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the interplay of conversational devices in building mutual understanding

Dideriksen¹,

Mh²,

Tylén³

et al. 2020

Preprint

View full text Add to dashboard Cite

Humans readily engage in idle chat, heated discussions, and negotiate tough joint decisions without ever having to think twice about the different mechanisms they use to keep the conversation grounded in mutual understanding. However, current attempts at identifying and assessing the grounding mechanisms that make this possible are fragmented across disciplines and investigate single mechanisms within single contexts. We present a comprehensive conceptual framework to investigate and quantify conversational grounding mechanisms, and how they adjust to contextual demands. In three corpus studies, we systematically test the role of three grounding mechanisms, backchannels, repair, and interactive alignment. Contrasting affiliative (AC) and task-oriented (TOC) conversations between and within participants, we find that grounding mechanisms adaptively adjust to the increased need for precision in the latter: Across Study 1 and Study 2, we show that low-precision mechanisms such as backchannels are more frequent in AC, while more costly but higher-precision mechanisms, such as specific repairs, are more frequent in TOC. Further, TOC involve higher complementarity of contributions in terms of the content and perspective: lower semantic alignment, and less frequent (but richer) lexical and syntactic alignment. Crucially, in Study 3, these variations in the use of grounding mechanisms are shown to be adaptive: pairs of interlocutors that show stronger linguistic complementarity perform better across the two tasks. By combining motivated comparisons of several conversational contexts, and theoretically informed computational analyses of empirical and experimental data, the present work lays the foundations for a comprehensive conceptual framework of grounding mechanisms in conversation.

show abstract

Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance

Cited by 7 publications

References 35 publications

ALIGN: Analyzing linguistic interactions with generalizable techNiques—A Python library.

ALIGN: Analyzing linguistic interactions with generalizable techNiques—A Python library.

Quantifying the interplay of conversational devices in building mutual understanding.

Quantifying the interplay of conversational devices in building mutual understanding

Contact Info

Product

Resources

About