---
title: "When to Say What and How — paper by Miehle et al. (2022)"
description: "The authors test whether this human tendency also shows up when people talk to machines. 87 references, cited by 1 paper here."
authors: ["Juliana Miehle", "Wolfgang Minker", "Stefan Ultes"]
year: 2022
venue: "Dialogue & Discourse"
source: "https://journals.uic.edu/ojs/index.php/dad/article/view/11526"
doi: "10.5210/dad.2022.101"
licence: "Open access, © the authors (no per-article licence in the journal metadata)"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/miehle-2022-when-say-what-how-adapting/"
---
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# When to Say What and How: Adapting the Elaborateness and Indirectness of Spoken Dialogue Systems

**Authors:** Juliana Miehle, [Wolfgang Minker](https://md.research.elamur.ai/free/en/authors/wolfgang-minker/) and [Stefan Ultes](https://md.research.elamur.ai/free/en/authors/stefan-ultes/) · **Year:** 2022 · **Venue:** [Dialogue & Discourse](https://md.research.elamur.ai/free/en/venues/dnd/) ([Dialogue & Discourse 2022](https://md.research.elamur.ai/free/en/venues/dnd/2022/)) · **Publisher:** [Linguistic Society of America](https://md.research.elamur.ai/free/en/publishers/linguistic-society-of-america/) · **Licence:** Open access, © the authors (no per-article licence in the journal metadata)

**Links:** [Publisher's page](https://doi.org/10.5210/dad.2022.101) · [PDF](https://journals.uic.edu/ojs/index.php/dad/article/download/11526/10634) · [DOI 10.5210/dad.2022.101](https://doi.org/10.5210/dad.2022.101) · [OpenAlex](https://openalex.org/W4223442012)

**Citations:** 9 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 141,840 papers whose bibliographies we read

## Summary
Commercial voice assistants decide what content to deliver but mostly ignore how it is phrased, even though people in conversation tend to converge on each other's manner of speaking. The authors test whether this human tendency also shows up when people talk to machines. They focus on two style dimensions. Elaborateness is how much extra information accompanies the core message. Indirectness is how explicitly the speaker addresses the point.

After a broad survey of communication-style research, they analyse a multilingual dialogue data set. They find that the system's style correlates significantly with the style the user had just used.

On this basis they add two modules to the usual spoken dialogue system pipeline. One classifier recognises the user's style, and a second module chooses a suitable style for the system's reply. Both are neural networks trained with supervised learning on features that any dialogue system can compute while a conversation runs. Both beat a majority-class baseline, which suggests that style-adaptive dialogue management is practical without extra sensors or manual annotation at runtime.

## Key points
- The two styles studied are elaborateness and indirectness. Earlier work (Miehle et al. 2018b; Pragst et al. 2019) showed that both affect user satisfaction and how users perceive a dialogue.
- The standard spoken dialogue system architecture gains two components: a user communication style classifier and a system communication style selection module. Both are framed as supervised classification tasks.
- On a multilingual data set, the system's communication style is significantly correlated with the user's preceding style.
- The networks use only features that can be derived automatically during an ongoing interaction. Both components outperform a majority-class baseline.
- The article extends Miehle et al. (2020). It grounds the style dimensions in Grice's cooperative principle and its maxims (Quantity, Quality, Relation, …). It appeared in Dialogue & Discourse 13(1), 2022.

## Interesting ideas
- Treat "how it is said" as its own label axis, separate from content, and split it into two independent dimensions (amount of extra information vs. explicitness of the point), which makes style measurable rather than vague.
- Break adaptation into two steps: first recognise the partner's style, then choose your own style as a separate decision, so each step can be trained and inspected on its own.
- Use only features that the running system already produces, so the adaptive module needs no outside annotation at use time.
- Before building an adaptive component, check statistically that the behaviour it would exploit (style alignment) actually exists in the data, and always report a majority-class baseline.
- Ground elaborateness in Grice's Quantity maxims (say as much as required, but no more), turning a classic pragmatic principle into a tunable system parameter.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (frames): prag could tag each utterance with an indirectness level next to its speech act, so indirect requests and hints become explicit inputs to implicature reasoning.
- **I2** (back): generation could read the elaborateness of the question and then choose, as a separate and loggable step, how much supporting detail to add to the answer text.
- **I3** (learn): style or register classifiers could be fed solely by signals already in the UD tree and frames, keeping every prediction traceable to pipeline evidence.
- **I4** (learn): add majority-class and "no-adaptation" runs as standard negative controls when evaluating any new pragmatic or answering module.
- **I5** (answer): readers answering school tests or fable questions could use a Quantity-style setting to decide between a bare answer and an answer plus justification.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

Also on the byline: [Universität Ulm](https://ror.org/032000t02).

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🧭 Topics (1): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 401 works

## Cite

Cite as: Juliana Miehle, Wolfgang Minker and Stefan Ultes. 2022. When to Say What and How: Adapting the Elaborateness and Indirectness of Spoken Dialogue Systems. Dialogue & Discourse.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{miehle2022when,
  title = {{When to Say What and How: Adapting the Elaborateness and Indirectness of Spoken Dialogue Systems}},
  author = {Juliana Miehle, Wolfgang Minker and Stefan Ultes},
  booktitle = {Dialogue \& Discourse},
  year = {2022},
  doi = {10.5210/dad.2022.101},
  url = {https://doi.org/10.5210/dad.2022.101}
}
```

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## References

87 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Heike Adel and Hinrich Schütze. Exploring different dimensions of attention for uncertainty detection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 22–34, 2017. [↗](https://doi.org/10.48550/arxiv.1612.06549)
2. Amir Aly and Adriana Tapus. Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human–robot interaction. Autonomous Robots, 40(2):193–209, 2016. [↗](https://doi.org/10.1007/s10514-015-9444-1)
3. Elisabeth André, Thomas Rist, Susanne Van Mulken, Martin Klesen, and Stefan Baldes. The automated design of believable dialogues for animated presentation teams. Embodied Conversational Agents, pages 220–255, 2000. [↗](https://doi.org/10.7551/mitpress/2697.003.0010)
4. Malika Aubakirova and Mohit Bansal. [Interpreting neural networks to improve politeness comprehension](https://md.research.elamur.ai/free/en/papers/aubakirova-2016-interpreting-neural-networks-improve-politeness/). In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2035–2041, 2016.
5. Gene Ball and Jack Breese. Emotion and personality in a conversational agent. Embodied Conversational Agents, pages 189–219, 2000. [↗](https://doi.org/10.7551/mitpress/2697.003.0009)
6. Linda Bell, Joakim Gustafson, and Mattias Heldner. Prosodic adaptation in human-computer interaction. In Proceedings of ICPHS, volume 3, pages 833–836. Citeseer, 2003. [find](https://scholar.google.com/scholar?q=Linda+Bell%2C+Joakim+Gustafson%2C+and+Mattias+Heldner%2E+Prosodic+adaptation+in+human%2Dcomputer+interaction%2E+In+Proceedings+of+ICPHS%2C+volume+3%2C+pages+833%E2%80%93836%2E+Citeseer%2C+2003%2E)
7. Kirsten Bergmann, Holly P. Branigan, and Stefan Kopp. Exploring the alignment space–lexical and gestural alignment with real and virtual humans. Frontiers in ICT, 2:7, 2015. [↗](https://doi.org/10.3389/fict.2015.00007)
8. Holly P. Branigan and Jamie Pearson. Alignment in human-computer interaction. How People Talk to Computers, Robots, and Other Artificial Communication Partners, pages 140–156, 2006. [find](https://scholar.google.com/scholar?q=Holly+P%2E+Branigan+and+Jamie+Pearson%2E+Alignment+in+human%2Dcomputer+interaction%2E+How+People+Talk+to+Computers%2C+Robots%2C+and+Other+Artificial+Communication+Partners%2C+pages+140%E2%80%93156%2C+2006%2E)
9. Holly P. Branigan, Martin J. Pickering, and Alexandra A. Cleland. Syntactic co-ordination in dialogue. Cognition, 75(2):B13–B25, 2000. [↗](https://doi.org/10.1016/s0010-0277(99)00081-5)
10. Holly P. Branigan, Martin J. Pickering, Jamie Pearson, Janet F. McLean, and Clifford I. Nass. Syntactic alignment between computers and people: The role of belief about mental states. In Proceedings of the 25th Annual Conference of the Cognitive Science Society, pages 186–191. Lawrence Erlbaum Associates, 2003. [find](https://scholar.google.com/scholar?q=Holly+P%2E+Branigan%2C+Martin+J%2E+Pickering%2C+Jamie+Pearson%2C+Janet+F%2E+McLean%2C+and+Clifford+I%2E+Nass%2E+Syntactic+alignment+between+computers+and+people%3A+The+role+of+belief+about+mental+states%2E+In+Proceedings+o)
11. Holly P. Branigan, Martin J. Pickering, Jamie Pearson, and Janet F. McLean. Linguistic alignment between people and computers. Journal of Pragmatics, 42(9):2355–2368, 2010. [↗](https://doi.org/10.1016/j.pragma.2009.12.012)
12. Susan E. Brennan. Conversation with and through computers. User Modeling and User-Adapted Interaction, 1(1):67–86, 1991. [↗](https://doi.org/10.1007/bf00158952)
13. Susan E. Brennan. Lexical entrainment in spontaneous dialog. Proceedings of ISSD, 96:41–44, 1996. [find](https://scholar.google.com/scholar?q=Susan+E%2E+Brennan%2E+Lexical+entrainment+in+spontaneous+dialog%2E+Proceedings+of+ISSD%2C+96%3A41%E2%80%9344%2C+1996%2E)
14. Susan E. Brennan and Herbert H. Clark. Conceptual pacts and lexical choice in conversation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(6):1482, 1996. [↗](https://doi.org/10.1037//0278-7393.22.6.1482)
15. Susan E. Brennan and Justina O. Ohaeri. Effects of message style on users’ attributions toward agents. In Conference Companion on Human Factors in Computing Systems, pages 281–282, 1994. [find](https://scholar.google.com/scholar?q=Susan+E%2E+Brennan+and+Justina+O%2E+Ohaeri%2E+Effects+of+message+style+on+users%E2%80%99+attributions+toward+agents%2E+In+Conference+Companion+on+Human+Factors+in+Computing+Systems%2C+pages+281%E2%80%93282%2C+1994%2E)
16. Carsten Brockmann, Amy Isard, Jon Oberlander, and Michael White. Modelling alignment for affective dialogue. In Workshop on Adapting the Interaction Style to Affective Factors at the 10th International Conference on User Modeling, 2005. [find](https://scholar.google.com/scholar?q=Carsten+Brockmann%2C+Amy+Isard%2C+Jon+Oberlander%2C+and+Michael+White%2E+Modelling+alignment+for+affective+dialogue%2E+In+Workshop+on+Adapting+the+Interaction+Style+to+Affective+Factors+at+the+10th+Internationa)
17. Dara C. Bultman and Bonnie L. Svarstad. Effects of physician communication style on client medication beliefs and adherence with antidepressant treatment. Patient Education and Counseling, 40(2):173 – 185, 2000. [↗](https://doi.org/10.1016/s0738-3991(99)00083-x)
18. Judee K. Burgoon, Lesa A. Stern, and Leesa Dillman. Interpersonal Adaptation: Dyadic Interaction Patterns. Cambridge University Press, 1995. [find](https://scholar.google.com/scholar?q=Judee+K%2E+Burgoon%2C+Lesa+A%2E+Stern%2C+and+Leesa+Dillman%2E+Interpersonal+Adaptation%3A+Dyadic+Interaction+Patterns%2E+Cambridge+University+Press%2C+1995%2E)
19. Hendrik Buschmeier, Kirsten Bergmann, and Stefan Kopp. An alignment-capable microplanner for natural language generation. In Proceedings of the 12th European Workshop on Natural Language Generation, pages 82–89, 2009. [find](https://scholar.google.com/scholar?q=Hendrik+Buschmeier%2C+Kirsten+Bergmann%2C+and+Stefan+Kopp%2E+An+alignment%2Dcapable+microplanner+for+natural+language+generation%2E+In+Proceedings+of+the+12th+European+Workshop+on+Natural+Language+Generation%2C+p)
20. Jacob Cohen. Statistical power analysis for the behavioral sciences. Academic Press, 1977. [find](https://scholar.google.com/scholar?q=Jacob+Cohen%2E+Statistical+power+analysis+for+the+behavioral+sciences%2E+Academic+Press%2C+1977%2E)
21. Rachel Coulston, Sharon Oviatt, and Courtney Darves. Amplitude convergence in children’s conversational speech with animated personas. In Seventh International Conference on Spoken Language Processing, pages 2689–2692, 2002. [find](https://scholar.google.com/scholar?q=Rachel+Coulston%2C+Sharon+Oviatt%2C+and+Courtney+Darves%2E+Amplitude+convergence+in+children%E2%80%99s+conversational+speech+with+animated+personas%2E+In+Seventh+International+Conference+on+Spoken+Language+Processing)
22. Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts. [A computational approach to politeness with application to social factors](https://md.research.elamur.ai/free/en/papers/mizil-2013-computational-approach-politeness-application-social/). In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 250–259, Sofia, Bulgaria, 2013. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.1306.6078)
23. Courtney Darves and Sharon Oviatt. Adaptation of users’ spoken dialogue patterns in a conversational interface. In Proceedings of the 7th International Conference on Spoken Language Processing, pages 561–564, 2002. [find](https://scholar.google.com/scholar?q=Courtney+Darves+and+Sharon+Oviatt%2E+Adaptation+of+users%E2%80%99+spoken+dialogue+patterns+in+a+conversational+interface%2E+In+Proceedings+of+the+7th+International+Conference+on+Spoken+Language+Processing%2C+pages+)
24. Markus De Jong, Mariët Theune, and Dennis Hofs. Politeness and alignment in dialogues with a virtual guide. In Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1, pages 207–214, 2008. [find](https://scholar.google.com/scholar?q=Markus+De+Jong%2C+Marie%CC%88t+Theune%2C+and+Dennis+Hofs%2E+Politeness+and+alignment+in+dialogues+with+a+virtual+guide%2E+In+Proceedings+of+the+7th+International+Joint+Conference+on+Autonomous+Agents+and+Multiagen)
25. Emanuele Di Buccio, Massimo Melucci, and Federica Moro. Detecting verbose queries and improving information retrieval. Information Processing & Management, 50(2):342–360, 2014. [↗](https://doi.org/10.1016/j.ipm.2013.09.003)
26. Christine Doran, John Aberdeen, Laurie Damianos, and Lynette Hirschman. Comparing several aspects of human-computer and human-human dialogues. In Current and New Directions in Discourse and Dialogue, pages 133–159. Springer, 2003. [↗](https://doi.org/10.1007/978-94-010-0019-2_7)
27. Jeroen Dral, Dirk Heylen, and Rieks op den Akker. Detecting Uncertainty in Spoken Dialogues: An Exploratory Research for the Automatic Detection of Speaker Uncertainty by Using Prosodic Markers, pages 67–77. Springer Netherlands, 2011. [find](https://scholar.google.com/scholar?q=Jeroen+Dral%2C+Dirk+Heylen%2C+and+Rieks+op+den+Akker%2E+Detecting+Uncertainty+in+Spoken+Dialogues%3A+An+Exploratory+Research+for+the+Automatic+Detection+of+Speaker+Uncertainty+by+Using+Prosodic+Markers%2C+pages)
28. Candia Elliott, R. Jerry Adams, and Suganya Sockalingam. Multicultural toolkit: Toolkit for cross-cultural collaboration. Awesome Library. [http://www.awesomelibrary.org/multiculturaltoolkit.html](http://www.awesomelibrary.org/multiculturaltoolkit.html), 2016. Accessed: 2016-05-01. [↗](http://www.awesomelibrary.org/multiculturaltoolkit.html)
29. Kate Forbes-Riley and Diane J. Litman. Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor. Speech Communication, 53(9):1115 – 1136, 2011. [↗](https://doi.org/10.1016/j.specom.2011.02.006)
30. Simon Garrod and Anthony Anderson. Saying what you mean in dialogue: A study in conceptual and semantic co-ordination. Cognition, 27(2):181–218, 1987. [↗](https://doi.org/10.1016/0010-0277(87)90018-7)
31. Kenza Gharouit and El Habib Nfaoui. A comparison of classification algorithms for verbose queries detection using babelnet. In 2017 Intelligent Systems and Computer Vision (ISCV), pages 1–5. [find](https://scholar.google.com/scholar?q=Kenza+Gharouit+and+El+Habib+Nfaoui%2E+A+comparison+of+classification+algorithms+for+verbose+queries+detection+using+babelnet%2E+In+2017+Intelligent+Systems+and+Computer+Vision+%28ISCV%29%2C+pages+1%E2%80%935%2E)
32. Pranav Goel, Yoichi Matsuyama, Michael Madaio, and Justine Cassell. I think it might help if we multiply, and not add: Detecting indirectness in conversation. In 9th International Workshop on Spoken Dialogue System Technology, pages 27–40. Springer Singapore, 2019. [↗](https://doi.org/10.1007/978-981-13-9443-0_3)
33. Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. [Learning Word Vectors for 157 Languages](https://md.research.elamur.ai/free/en/papers/grave-2018-learning-word-vectors-157-languages/). In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), 2018.
34. Herbert P. Grice. Logic and conversation. In Speech Acts, pages 41–58. Brill, 1975. [↗](https://doi.org/10.1163/9789004368811_003)
35. Swati Gupta, Marilyn A. Walker, and Daniela M. Romano. How rude are you?: Evaluating politeness and affect in interaction. In International Conference on Affective Computing and Intelligent Interaction, pages 203–217. Springer, 2007. [find](https://scholar.google.com/scholar?q=Swati+Gupta%2C+Marilyn+A%2E+Walker%2C+and+Daniela+M%2E+Romano%2E+How+rude+are+you%3F%3A+Evaluating+politeness+and+affect+in+interaction%2E+In+International+Conference+on+Affective+Computing+and+Intelligent+Interactio)
36. Rens Hoegen, Deepali Aneja, Daniel McDuff, and Mary Czerwinski. An end-to-end conversational style matching agent. In Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents, pages 111–118, 2019. [find](https://scholar.google.com/scholar?q=Rens+Hoegen%2C+Deepali+Aneja%2C+Daniel+McDuff%2C+and+Mary+Czerwinski%2E+An+end%2Dto%2Dend+conversational+style+matching+agent%2E+In+Proceedings+of+the+19th+ACM+International+Conference+on+Intelligent+Virtual+Agents)
37. Dennis Hofs, Mariët Theune, and Rieks op den Akker. Natural interaction with a virtual guide in a virtual environment. Journal on Multimodal User Interfaces, 3(1-2):141–153, 2010. [↗](https://doi.org/10.1007/s12193-009-0024-6)
38. Geert Hofstede. Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations. Sage, 2009. [find](https://scholar.google.com/scholar?q=Geert+Hofstede%2E+Culture%E2%80%99s+Consequences%3A+Comparing+Values%2C+Behaviors%2C+Institutions+and+Organizations+Across+Nations%2E+Sage%2C+2009%2E)
39. Thomas Holtgraves. Language structure in social interaction: Perceptions of direct and indirect speech acts and interactants who use them. Journal of Personality and Social Psychology, 51(2): 305, 1986. [↗](https://doi.org/10.1037//0022-3514.51.2.305)
40. Zhichao Hu, Jean E. Fox Tree, and Marilyn A. Walker. Modeling linguistic and personality adaptation for natural language generation. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 20–31, 2018. [find](https://scholar.google.com/scholar?q=Zhichao+Hu%2C+Jean+E%2E+Fox+Tree%2C+and+Marilyn+A%2E+Walker%2E+Modeling+linguistic+and+personality+adaptation+for+natural+language+generation%2E+In+Proceedings+of+the+19th+Annual+SIGdial+Meeting+on+Discourse+and+)
41. Bahar Irfan, Anika Narayanan, and James Kennedy. Dynamic emotional language adaptation in multiparty interactions with agents. In Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, pages 1–8, 2020. [find](https://scholar.google.com/scholar?q=Bahar+Irfan%2C+Anika+Narayanan%2C+and+James+Kennedy%2E+Dynamic+emotional+language+adaptation+in+multiparty+interactions+with+agents%2E+In+Proceedings+of+the+20th+ACM+International+Conference+on+Intelligent+Vi)
42. Katherine Isbister and Clifford Nass. Consistency of personality in interactive characters: verbal cues, non-verbal cues, and user characteristics. International Journal of Human-Computer Studies, 53(2):251–267, 2000. [↗](https://doi.org/10.1006/ijhc.2000.0368)
43. W. Lewis Johnson, Paola Rizzo, Wauter Bosma, Sander Kole, Mattijs Ghijsen, and Herwin van Welbergen. Generating socially appropriate tutorial dialog. In Affective Dialogue Systems, pages 254–264. Springer Berlin Heidelberg, 2004. [find](https://scholar.google.com/scholar?q=W%2E+Lewis+Johnson%2C+Paola+Rizzo%2C+Wauter+Bosma%2C+Sander+Kole%2C+Mattijs+Ghijsen%2C+and+Herwin+van+Welbergen%2E+Generating+socially+appropriate+tutorial+dialog%2E+In+Affective+Dialogue+Systems%2C+pages+254%E2%80%93264%2E+Spri)
44. Melissa K. Jungers, Caroline Palmer, and Shari R. Speer. Time after time: The coordinating influence of tempo in music and speech. Cognitive Processing, 1(2):21–35, 2002. [find](https://scholar.google.com/scholar?q=Melissa+K%2E+Jungers%2C+Caroline+Palmer%2C+and+Shari+R%2E+Speer%2E+Time+after+time%3A+The+coordinating+influence+of+tempo+in+music+and+speech%2E+Cognitive+Processing%2C+1%282%29%3A21%E2%80%9335%2C+2002%2E)
45. Robert B. Kaplan. Cultural thought patterns in inter-cultural education. Language Learning, 16 (1-2):1–20, 1966. [↗](https://doi.org/10.1111/j.1467-1770.1966.tb00804.x)
46. Theodora Koulouri, Stanislao Lauria, and Robert D. Macredie. Do (and say) as i say: Linguistic adaptation in human–computer dialogs. Human–Computer Interaction, 31(1):59–95, 2016. [find](https://scholar.google.com/scholar?q=Theodora+Koulouri%2C+Stanislao+Lauria%2C+and+Robert+D%2E+Macredie%2E+Do+%28and+say%29+as+i+say%3A+Linguistic+adaptation+in+human%E2%80%93computer+dialogs%2E+Human%E2%80%93Computer+Interaction%2C+31%281%29%3A59%E2%80%9395%2C+2016%2E)
47. Paul R. Kroeger. Analyzing meaning: An introduction to semantics and pragmatics. Language Science Press, 2019. [↗](https://doi.org/10.5281/zenodo.2538329)
48. Ivana Kruijff-Korbayová, Ciprian Kukina, Gerstenberger Olga, and Jan Schehl. Generation of output style variation in the sammie dialogue system. In Proceedings of the Fifth International Natural Language Generation Conference, pages 129–137, 2008. [find](https://scholar.google.com/scholar?q=Ivana+Kruijff%2DKorbayova%CC%81%2C+Ciprian+Kukina%2C+Gerstenberger+Olga%2C+and+Jan+Schehl%2E+Generation+of+output+style+variation+in+the+sammie+dialogue+system%2E+In+Proceedings+of+the+Fifth+International+Natural+Lang)
49. Willem J. M. Levelt and Stephanie Kelter. Surface form and memory in question answering. Cognitive Psychology, 14(1):78–106, 1982. [↗](https://doi.org/10.1016/0010-0285(82)90005-6)
50. Richard D. Lewis. When Cultures Collide: Leading Across Cultures. Brealey, 2010. [find](https://scholar.google.com/scholar?q=Richard+D%2E+Lewis%2E+When+Cultures+Collide%3A+Leading+Across+Cultures%2E+Brealey%2C+2010%2E)
51. Jackson Liscombe, Julia Hirschberg, and Jennifer J. Venditti. Detecting certainness in spoken tutorial dialogues. In Proceedings of the 9th European Conference on Speech Communication and Technology, pages 1837–1840, 2005. [find](https://scholar.google.com/scholar?q=Jackson+Liscombe%2C+Julia+Hirschberg%2C+and+Jennifer+J%2E+Venditti%2E+Detecting+certainness+in+spoken+tutorial+dialogues%2E+In+Proceedings+of+the+9th+European+Conference+on+Speech+Communication+and+Technology%2C+)
52. Michael Madaio, Justine Cassell, and Amy Ogan. The impact of peer tutors’ use of indirect feedback and instructions. In Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning. Philadelphia, PA: International Society of the Learning Sciences, 2017. [↗](https://doi.org/10.22318/cscl2017.52)
53. François Mairesse and Marilyn A. Walker. Towards personality-based user adaptation: psychologically informed stylistic language generation. User Modeling and User-Adapted Interaction, 20 (3):227–278, 2010. [↗](https://doi.org/10.1007/s11257-010-9076-2)
54. François Mairesse and Marilyn A. Walker. Controlling user perceptions of linguistic style: Trainable generation of personality traits. Computational Linguistics, 37(3):455–488, 2011. [↗](https://doi.org/10.1162/coli_a_00063)
55. Juliana Miehle, Koichiro Yoshino, Louisa Pragst, Stefan Ultes, Satoshi Nakamura, and Wolfgang Minker. Cultural communication idiosyncrasies in human-computer interaction. In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 74–79, Los Angeles, USA, September 2016. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Juliana+Miehle%2C+Koichiro+Yoshino%2C+Louisa+Pragst%2C+Stefan+Ultes%2C+Satoshi+Nakamura%2C+and+Wolfgang+Minker%2E+Cultural+communication+idiosyncrasies+in+human%2Dcomputer+interaction%2E+In+Proceedings+of+the+17th+An)
56. Juliana Miehle, Wolfgang Minker, and Stefan Ultes. What causes the differences in communication styles? a multicultural study on directness and elaborateness. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), 2018a. [find](https://scholar.google.com/scholar?q=Juliana+Miehle%2C+Wolfgang+Minker%2C+and+Stefan+Ultes%2E+What+causes+the+differences+in+communication+styles%3F+a+multicultural+study+on+directness+and+elaborateness%2E+In+Proceedings+of+the+Eleventh+Internatio)
57. Juliana Miehle, Wolfgang Minker, and Stefan Ultes. Exploring the impact of elaborateness and indirectness on user satisfaction in a spoken dialogue system. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP), pages 165–172. ACM, 2018b. [find](https://scholar.google.com/scholar?q=Juliana+Miehle%2C+Wolfgang+Minker%2C+and+Stefan+Ultes%2E+Exploring+the+impact+of+elaborateness+and+indirectness+on+user+satisfaction+in+a+spoken+dialogue+system%2E+In+Adjunct+Publication+of+the+26th+Conferenc)
58. Juliana Miehle, Isabel Feustel, Julia Hornauer, Wolfgang Minker, and Stefan Ultes. Estimating user communication styles for spoken dialogue systems. In Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020), pages 533–541. European Language Resources Association (ELRA), May 2020. [find](https://scholar.google.com/scholar?q=Juliana+Miehle%2C+Isabel+Feustel%2C+Julia+Hornauer%2C+Wolfgang+Minker%2C+and+Stefan+Ultes%2E+Estimating+user+communication+styles+for+spoken+dialogue+systems%2E+In+Proceedings+of+the+12th+International+Conference)
59. Youngme Moon and Clifford Nass. How “real” are computer personalities? psychological responses to personality types in human-computer interaction. Communication Research, 23(6):651–674, 1996. [find](https://scholar.google.com/scholar?q=Youngme+Moon+and+Clifford+Nass%2E+How+%E2%80%9Creal%E2%80%9D+are+computer+personalities%3F+psychological+responses+to+personality+types+in+human%2Dcomputer+interaction%2E+Communication+Research%2C+23%286%29%3A651%E2%80%93674%2C+1996%2E)
60. Clifford Nass, Youngme Moon, Brian J. Fogg, Byron Reeves, and Chris Dryer. Can computer personalities be human personalities? In Conference Companion on Human Factors in Computing Systems, pages 228–229, 1995. [↗](https://doi.org/10.1006/ijhc.1995.1042)
61. Ani Nenkova, Agustin Gravano, and Julia Hirschberg. High frequency word entrainment in spoken dialogue. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, pages 169–172. Association for Computational Linguistics, 2008. [find](https://scholar.google.com/scholar?q=Ani+Nenkova%2C+Agustin+Gravano%2C+and+Julia+Hirschberg%2E+High+frequency+word+entrainment+in+spoken+dialogue%2E+In+Proceedings+of+the+46th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+on+Hu)
62. James W. Neuliep. Intercultural Communication: A Contextual Approach. SAGE, 2018. [find](https://scholar.google.com/scholar?q=James+W%2E+Neuliep%2E+Intercultural+Communication%3A+A+Contextual+Approach%2E+SAGE%2C+2018%2E)
63. Dat Quoc Nguyen, Dai Quoc Nguyen, Dang Duc Pham, and Son Bao Pham. Rdrpostagger: A ripple down rules-based part-of-speech tagger. In Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 17–20, 2014. [↗](https://doi.org/10.60692/my05x-h3296)
64. Kate G. Niederhoffer and James W. Pennebaker. Linguistic style matching in social interaction. Journal of Language and Social Psychology, 21(4):337–360, 2002. [↗](https://doi.org/10.1177/026192702237953)
65. Shereen Oraby, Lena Reed, Shubhangi Tandon, T. S. Sharath, Stephanie Lukin, and Marilyn A. Walker. Controlling personality-based stylistic variation with neural natural language generators. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 180–190, 2018. [find](https://scholar.google.com/scholar?q=Shereen+Oraby%2C+Lena+Reed%2C+Shubhangi+Tandon%2C+T%2E+S%2E+Sharath%2C+Stephanie+Lukin%2C+and+Marilyn+A%2E+Walker%2E+Controlling+personality%2Dbased+stylistic+variation+with+neural+natural+language+generators%2E+In+Proceed)
66. Sharon Oviatt, Courtney Darves, and Rachel Coulston. Toward adaptive conversational interfaces: Modeling speech convergence with animated personas. ACM Transactions on Computer-Human Interaction (TOCHI), 11(3):300–328, 2004. [find](https://scholar.google.com/scholar?q=Sharon+Oviatt%2C+Courtney+Darves%2C+and+Rachel+Coulston%2E+Toward+adaptive+conversational+interfaces%3A+Modeling+speech+convergence+with+animated+personas%2E+ACM+Transactions+on+Computer%2DHuman+Interaction+%28TOCH)
67. Jennifer S. Pardo. On phonetic convergence during conversational interaction. The Journal of the Acoustical Society of America, 119(4):2382–2393, 2006. [↗](https://doi.org/10.1121/1.2178720)
68. Jamie Pearson, Jiang Hu, Holly P. Branigan, Martin J. Pickering, and Clifford I. Nass. Adaptive language behavior in hci: how expectations and beliefs about a system affect users’ word choice. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1177– 1180, 2006. [find](https://scholar.google.com/scholar?q=Jamie+Pearson%2C+Jiang+Hu%2C+Holly+P%2E+Branigan%2C+Martin+J%2E+Pickering%2C+and+Clifford+I%2E+Nass%2E+Adaptive+language+behavior+in+hci%3A+how+expectations+and+beliefs+about+a+system+affect+users%E2%80%99+word+choice%2E+In+Proc)
69. Robin Pesch, Ricarda B. Bouncken, and Sascha Kraus. Effects of communication style and age diversity in innovation teams. International Journal of Innovation and Technology Management, 12(06):1–20, 2015. [↗](https://doi.org/10.1142/s0219877015500297)
70. Martin J. Pickering and Simon Garrod. Toward a mechanistic psychology of dialogue. Behavioral and Brain Sciences, 27(2):169–190, 2004. [↗](https://doi.org/10.1017/s0140525x04000056)
71. Kaśka Porayska-Pomsta and Chris Mellish. Modelling politeness in natural language generation. In International Conference on Natural Language Generation, pages 141–150. Springer, 2004. [find](https://scholar.google.com/scholar?q=Kas%CC%81ka+Porayska%2DPomsta+and+Chris+Mellish%2E+Modelling+politeness+in+natural+language+generation%2E+In+International+Conference+on+Natural+Language+Generation%2C+pages+141%E2%80%93150%2E+Springer%2C+2004%2E)
72. Louisa Pragst, Wolfgang Minker, and Stefan Ultes. Exploring the applicability of elaborateness and indirectness in dialogue management. In Advanced Social Interaction with Agents : 8th International Workshop on Spoken Dialog Systems, pages 189–198. Springer International Publishing, 2019. [↗](https://doi.org/10.1007/978-3-319-92108-2_20)
73. Anna Prokofieva and Julia Hirschberg. Hedging and speaker commitment. In Proceedings of the 5th International Workshop on Emotion, Social Signals, Sentiment & Linked Open Data, Reykjavik, Iceland, pages 10–13, 2014. [find](https://scholar.google.com/scholar?q=Anna+Prokofieva+and+Julia+Hirschberg%2E+Hedging+and+speaker+commitment%2E+In+Proceedings+of+the+5th+International+Workshop+on+Emotion%2C+Social+Signals%2C+Sentiment+%26+Linked+Open+Data%2C+Reykjavik%2C+Iceland%2C+pag)
74. David Reitter, Frank Keller, and Johanna D. Moore. Computational modelling of structural priming in dialogue. In Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, pages 121–124. Association for Computational Linguistics, 2006. [find](https://scholar.google.com/scholar?q=David+Reitter%2C+Frank+Keller%2C+and+Johanna+D%2E+Moore%2E+Computational+modelling+of+structural+priming+in+dialogue%2E+In+Proceedings+of+the+Human+Language+Technology+Conference+of+the+NAACL%2C+Companion+Volume%3A)
75. Michael F. Schober. Spatial perspective-taking in conversation. Cognition, 47(1):1–24, 1993. [↗](https://doi.org/10.1016/0010-0277(93)90060-9)
76. John R. Searle. Indirect speech acts. In Syntax and Semantics 3. Speech Acts, pages 59–82. Academic Press, 1975. [↗](https://doi.org/10.1163/9789004368811_004)
77. Tuva Lunde Smestad and Frode Volden. Chatbot personalities matters. In Internet Science, pages 170–181. Springer International Publishing, 2019. [find](https://scholar.google.com/scholar?q=Tuva+Lunde+Smestad+and+Frode+Volden%2E+Chatbot+personalities+matters%2E+In+Internet+Science%2C+pages+170%E2%80%93181%2E+Springer+International+Publishing%2C+2019%2E)
78. Svetlana Stenchikova and Amanda Stent. Measuring adaptation between dialogs. In Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue, pages 166–173, 2007. [find](https://scholar.google.com/scholar?q=Svetlana+Stenchikova+and+Amanda+Stent%2E+Measuring+adaptation+between+dialogs%2E+In+Proceedings+of+the+8th+SIGdial+Workshop+on+Discourse+and+Dialogue%2C+pages+166%E2%80%93173%2C+2007%2E)
79. Stanley S. Stevens. On the theory of scales of measurement. Science, 103(2684):677–680, 1946. [↗](https://doi.org/10.1126/science.103.2684.677)
80. Noriko Suzuki and Yasuhiro Katagiri. Prosodic alignment in human–computer interaction. Connection Science, 19(2):131–141, 2007. [↗](https://doi.org/10.1080/09540090701369125)
81. Adriana Tapus and Maja J. Mataric. Socially assistive robots: The link between personality, empathy, physiological signals, and task performance. In AAAI Spring Symposium: Emotion, Personality, and Social Behavior, pages 133–140, 2008. [find](https://scholar.google.com/scholar?q=Adriana+Tapus+and+Maja+J%2E+Mataric%2E+Socially+assistive+robots%3A+The+link+between+personality%2C+empathy%2C+physiological+signals%2C+and+task+performance%2E+In+AAAI+Spring+Symposium%3A+Emotion%2C+Personality%2C+and+So)
82. Morgan Ulinski, Seth Benjamin, and Julia Hirschberg. [Using hedge detection to improve committed belief tagging](https://md.research.elamur.ai/free/en/papers/ulinski-2018-hedge-detection-improve-committed-belief/). In Proceedings of the Workshop on Computational Semantics beyond Events and Roles, pages 1–5, 2018.
83. Willemijn M. Van Dolen, Pratibha A. Dabholkar, and Ko De Ruyter. Satisfaction with online commercial group chat: the influence of perceived technology attributes, chat group characteristics, and advisor communication style. Journal of Retailing, 83(3):339–358, 2007. [↗](https://doi.org/10.1016/j.jretai.2007.03.004)
84. Marilyn A. Walker, Amanda Stent, François Mairesse, and Rashmi Prasad. Individual and domain adaptation in sentence planning for dialogue. Journal of Artificial Intelligence Research, 30: 413–456, 2007. [↗](https://doi.org/10.1613/jair.2329)
85. Ning Wang, W. Lewis Johnson, Richard E. Mayer, Paola Rizzo, Erin Shaw, and Heather Collins. The politeness effect: Pedagogical agents and learning gains. In AIED, pages 686–693, 2005. [find](https://scholar.google.com/scholar?q=Ning+Wang%2C+W%2E+Lewis+Johnson%2C+Richard+E%2E+Mayer%2C+Paola+Rizzo%2C+Erin+Shaw%2C+and+Heather+Collins%2E+The+politeness+effect%3A+Pedagogical+agents+and+learning+gains%2E+In+AIED%2C+pages+686%E2%80%93693%2C+2005%2E)
86. Stephen Whittaker, Marilyn A. Walker, and Preetam Maloor. Should I tell all?: An experiment on conciseness in spoken dialogue. In Eighth European Conference on Speech Communication and Technology, pages 1685–1688, 2003. [find](https://scholar.google.com/scholar?q=Stephen+Whittaker%2C+Marilyn+A%2E+Walker%2C+and+Preetam+Maloor%2E+Should+I+tell+all%3F%3A+An+experiment+on+conciseness+in+spoken+dialogue%2E+In+Eighth+European+Conference+on+Speech+Communication+and+Technology%2C+pag)
87. Jenny Wilkie, Mervyn A. Jack, and Peter J. Littlewood. System-initiated digressive proposals in automated human–computer telephone dialogues: the use of contrasting politeness strategies. International Journal of Human-Computer Studies, 62(1):41–71, 2005. [↗](https://doi.org/10.1016/j.ijhcs.2004.08.001)

*Updated 24 October 2026*