A Strategy for Information Presentation in Spoken Dialog Systems
Open accessAbstract
In spoken dialog systems, information must be presented sequentially, making it difficult to quickly browse through a large number of options. Recent studies have shown that user satisfaction is negatively correlated with dialog duration, suggesting that systems should be designed to maximize the efficiency of the interactions. Analysis of the logs of 2,000 dialogs between users and nine different dialog systems reveals that a large percentage of the time is spent on the information presentation phase, thus there is potentially a large pay-off to be gained from optimizing information presentation in spoken dialog systems. This article proposes a method that improves the efficiency of coping with large numbers of diverse options by selecting options and then structuring them based on a model of the user's preferences. This enables the dialog system to automatically determine trade-offs between alternative options that are relevant to the user and present these trade-offs explicitly. Multiple attractive options are thereby structured such that the user can gradually refine her request to find the optimal trade-off. To evaluate and challenge our approach, we conducted a series of experiments that test the effectiveness of the proposed strategy. Experimental results show that basing the content structuring and content selection process on a user model increases the efficiency and effectiveness of the user's interaction. Users complete their tasks more successfully and more quickly. Furthermore, user surveys revealed that participants found that the user-model based system presents complex trade-offs understandably and increases overall user satisfaction. The experiments also indicate that presenting users with a brief overview of options that do not fit their requirements significantly improves the user's overview of available options, also making them feel more confident in having been presented with all relevant options.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The effects of personalization have been evaluated by measuring efficiency, user satisfaction, relevance, understandability, information quality, and appropriateness of system responses.
“The effects of personalization have been evaluated in various ways by measuring aspects like efficiency in terms of the number and duration of interactions [11,20], user satisfaction, relevance, and understandability [20], information quality presented [21], and appropriateness of system responses [10].”
From The Personalization of Conversational Agents in Health Care: Systematic Review (2019) · cites this paper as [20]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Eye movements in reading and information processing: 20 years of research
Rayner, K. (1998) · Psychological Bulletin · doi:10.1037/0033-2909.124.3.372 · Cited by 7,979 · see citing works
Linguistic complexity: locality of syntactic dependencies
Gibson, E. (1998) · Cognition · doi:10.1016/s0010-0277(98)00034-1 · Cited by 2,537 · see citing works
Eye movements in reading and information processing.
Rayner, K. (1978) · Psychological Bulletin · doi:10.1037/0033-2909.85.3.618 · Cited by 1,119 · see citing works
User Modeling via Stereotypes*
Rich, E. A. (1979) · Cognitive Science · doi:10.1207/s15516709cog0304_3 · Cited by 1,020 · see citing works
Contextual effects on word perception and eye movements during reading
Ehrlich, S. F., & Rayner, K. (1981) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(81)90220-6 · Cited by 796 · see citing works
Wizard of Oz studies — why and how
Dahlbäck, N., Jönsson, A., & Ahrenberg, L. (1993) · Knowledge-Based Systems · doi:10.1016/0950-7051(93)90017-n · Cited by 706 · see citing works
Information Structure and the Syntax-Phonology Interface
Steedman, M. J. (2000) · Linguistic Inquiry · doi:10.1162/002438900554505 · Cited by 669 · see citing works
(2005) · Cited by 505 · see citing works
The open agent architecture: A framework for building distributed software systems
Martin, D. L., Cheyer, A. J., & Moran, D. B. (1999) · Applied Artificial Intelligence · doi:10.1080/088395199117504 · Cited by 495 · see citing works
Eye movements and on-line language comprehension processes
Rayner, K., Sereno, S. C., Morris, R. K., Schmauder, A. R., & Clifton, C. (1989) · Language and Cognitive Processes · doi:10.1080/01690968908406362 · Cited by 468 · see citing works
Unleashing the killer corpus: experiences in creating the multi-everything AMI Meeting Corpus
Carletta, J. (2007) · Computers and the Humanities · doi:10.1007/s10579-007-9040-x · Cited by 362 · see citing works
Interactive Assessment of User Preference Models: The Automated Travel Assistant
Linden, G., Hanks, S., & Lesh, N. B. (1997) · Springer eBooks · doi:10.1007/978-3-7091-2670-7_9 · Cited by 230 · see citing works
A Personalized System for Conversational Recommendations
Thompson, C. A., Goker, M. H., & Langley, P. (2004) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1318 · Cited by 223 · see citing works
Ervin, B., Simon, N., Weill, K., Tucker, G., Parlan, H., & Harewood, A. (1987) · Medical Entomology and Zoology · Cited by 208 · see citing works
Quantitative and qualitative evaluation of Darpa Communicator spoken dialogue systems
Walker, M. A., Passonneau, R. J., & Boland, J. E. (2001) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1073012.1073078 · Cited by 202 · see citing works
Rayner, K., Binder, K. S., Ashby, J., & Pollatsek, A. (2001) · Vision Research · doi:10.1016/s0042-6989(00)00310-2 · Cited by 143 · see citing works
DIPPER : Description and formalisation of an information-state update dialogue system architecture
Bos, J., Klein, E., Lemon, O., & Oka, T. (2003) · Cited by 125 · see citing works
Generation and evaluation of user tailored responses in multimodal dialogue
Walker, M. A., Whittaker, S. J., Stent, A., Maloor, P., Moore, J., Johnston, M., & Vasireddy, G. (2004) · Cognitive Science · doi:10.1016/j.cogsci.2004.06.002 · Cited by 125 · see citing works
Self-citationResponse planning and generation in the mercury flight reservation system
Seneff, S. (2002) · Computer Speech & Language · doi:10.1016/s0885-2308(02)00011-6 · Cited by 82 · see citing works
Developing a flexible spoken dialog system using simulation
Chung, G. (2004) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1218955.1218964 · Cited by 80 · see citing works
Evaluating information presentation strategies for spoken recommendations
Winterboer, A., & Moore, J. D. (2007) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1297231.1297260 · Cited by 58 · see citing works
Self-citationTowards the automatic generation of mixed-initiative dialogue systems from web content
Polifroni, J. H., Chung, G., & Seneff, S. (2003) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2003-97 · Cited by 56 · see citing works
Vauras, M., Hyönä, J., & Niemi, P. (1992) · Journal of Research in Reading · doi:10.1111/j.1467-9817.1992.tb00020.x · Cited by 55 · see citing works
Information Presentation in Spoken Dialogue Systems
Demberg, V., & Moore, J. D. (2006) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 52 · see citing works
Self-citationWalker, M. A., & Passonneau, R. J. (2001) · International Conference on Human Language Technology Research · doi:10.3115/1072133.1072148 · Cited by 51 · see citing works
Eye Movements and Lexical Access in Reading
Rayner, A. P. &. K. (2012) · Routledge eBooks · doi:10.4324/9780203052389-12 · Cited by 47 · see citing works
An empirical study of the influence of user tailoring on evaluative argument effectiveness
Carenini, G., & Moore, J. D. (2001) · Cited by 42 · see citing works
Self-citationAutomatic annotation of context and speech acts for dialogue corpora
Georgila, K., Lemon, O., Henderson, J., & Moore, J. D. (2009) · Natural Language Engineering · doi:10.1017/s1351324909005105 · Cited by 39 · see citing works
Self-citationFlexible guidance generation using user model in spoken dialogue systems
Komatani, K., Ueno, S., Kawahara, T., & Okuno, H. G. (2003) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1075096.1075129 · Cited by 32 · see citing works
Intensional Summaries as Cooperative Responses in Dialogue: Automation and Evaluation
Polifroni, J. H., & Walker, M. A. (2008) · Cited by 30 · see citing works
Generating Tailored, Comparative Descriptions with Contextually Appropriate Intonation
White, M., Clark, R. A., & Moore, J. D. (2010) · Computational Linguistics · doi:10.1162/coli.09-023-r1-08-002 · Cited by 29 · see citing works
Self-citationA constraint-based model for cooperative response generation in information dialogues
Qu, Y., & Beale, S. (1999) · Cited by 15 · see citing works
Hu, J., Winterboer, A., Nass, C., Moore, J., & Illowsky, R. (2007) · International Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240827 · Cited by 15 · see citing works
Self-citationInteractive question answering and constraint relaxation in spoken dialogue systems
Varges, S., Weng, F., & Pon-Barry, H. (2006) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.3115/1654595.1654601 · Cited by 14 · see citing works
Antecedent typicality effects in the processing of noun phrase anaphors
van-Gompel, R., Liversedge, S. P., & Pearson, J. N. (2004) · Cited by 11 · see citing works
The influence of user tailoring and cognitive load on user performance in spoken dialogue systems
Winterboer, A. K., Hu, J., Moore, J. D., & Nass, C. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-67 · Cited by 10 · see citing works
Self-citationEvaluating the effectiveness of information presentation in a full end-to-end dialogue system
Paksima, T., Georgila, K., & Moore, J. D. (2009) · doi:10.3115/1708376.1708377 · Cited by 10 · see citing works
Self-citationWinterboer, A. K., Tietze, M. I., Wolters, M. K., & Moore, J. D. (2010) · Computer Speech & Language · doi:10.1016/j.csl.2010.04.003 · Cited by 9 · see citing works
Self-citationShould i tell all?: an experiment on conciseness in spoken dialogue
Whittaker, S. G., Walker, M. A., & Maloor, P. (2003) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2003-482 · Cited by 8 · see citing works
Evaluation of content presentation strategies for an in-car spoken dialogue system
Pon-Barry, H., Weng, F., & Varges, S. (2006) · Interspeech (USB) · doi:10.21437/interspeech.2006-530 · Cited by 8 · see citing works
An intelligent procedure assistant built using REGULUS 2 and ALTERF
Rayner, M., Hockey, B. A., Hieronymus, J., Dowding, J., Aist, G., & Early, S. (2003) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1075178.1075214 · Cited by 8 · see citing works
Clarissa Spoken Dialogue System for Procedure Reading and Navigation
Hieronymus, J. L., & Dowding, J. (2004) · NASA Technical Reports Server (NASA) · Cited by 5 · see citing works
Evaluating Information Presentation Strategies for Spoken Dialogue Systems
Winterboer, A. K. (2009) · ERA · Cited by 2 · see citing works
Self-citation(2006) · Routledge eBooks · doi:10.4324/9780203961414-13 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 45 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- [15]15. Moore, Johanna D. 2006. Natural language generation for information presentation. Paper presented at the Spoken Language Technology Workshop SLT '06, December, Aruba.
- [16]16. Moore, Johanna D., Mary Ellen Foster, Oliver Lemon, and Michael White. 2004. Generating tailored, comparative descriptions in spoken dialogue. In Proceedings of the Seventeenth International Florida Artificial Intelligence Research Sociey Conference, AAAI Press, pages 917-922, Miami Beach, FL.
- [22]22. In D. A. Balota, G. B. Flores d'Arcais, and K. Rayner, editors, Comprehension Processes in Reading. Erlbaum, Hillsdale, NJ, pages 143-163.
- [37]37. Walker, Marilyn and Rebecca Passonneau. 2001. Date: A dialogue act tagging scheme for evaluation of spoken dialogue systems. In HLT '01: Proceedings of the First International Conference on Human Language Technology Research, pages 1-8, Morristown, NJ.
Cited by
Sorted by citations
The Personalization of Conversational Agents in Health Care: Systematic Review
Kocaballi, A. B., Berkovsky, S., Quiroz, J. C., Laranjo, L., Tong, H. L., Rezazadegan, D., Briatore, A., & Coiera, E. (2019) · Journal of Medical Internet Research · doi:10.2196/15360 · Cited by 332 · see citing works
“Furthermore, personalization can be an essential system feature for voice interfaces due to the limitations in presenting large amounts of information through a voice-only modality [20].”
BackgroundUX Research on Conversational Human-AI Interaction: A Literature Review of the ACM Digital Library
Zheng, Q., Tang, Y., Liu, Y., Liu, W., & Huang, Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501855 · Cited by 114 · see citing works
Alimamy, S., & Kuhail, M. A. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107711 · Cited by 74 · see citing works
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
Balakrishnan, A., Rao, J., Upasani, K., White, M., & Subba, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1080 · Cited by 70 · see citing works
Trippas, J. R., Spina, D., Sanderson, M., & Cavedon, L. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767826 · Cited by 43 · see citing works
Adaptive Generation in Dialogue Systems Using Dynamic User Modeling
Janarthanam, S., & Lemon, O. (2014) · Computational Linguistics · doi:10.1162/coli_a_00203 · Cited by 39 · see citing works
Results Presentation Methods for a Spoken Conversational Search System
Trippas, J. R., Spina, D., Sanderson, M., & Cavedon, L. (2015) · doi:10.1145/2810355.2810356 · Cited by 36 · see citing works
Rieser, V., Lemon, O., & Keizer, S. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2014.2315271 · Cited by 35 · see citing works
Consumers' responses to personalized service from medical artificial intelligence and human doctors
Zhang, Y., Tan, W., & Lee, E. (2023) · Psychology and Marketing · doi:10.1002/mar.21911 · Cited by 33 · see citing works
Chatbots as Cognitive, Educational, Advisory & Coaching Systems
Karyotaki, M., Drigas, A. S., & Skianis, C. A. (2022) · Technium Social Sciences Journal · doi:10.47577/tssj.v30i1.6277 · Cited by 33 · see citing works
A Mixed-Method Analysis of Text and Audio Search Interfaces with Varying Task Complexity
Vtyurina, A., Clarke, C. L. A., Law, E., Trippas, J. R., & Bota, H. (2020) · Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval · doi:10.1145/3409256.3409822 · Cited by 29 · see citing works
Shen, H., & Wang, M. (2021) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2021.1921367 · Cited by 25 · see citing works
Collecting Qualitative Data at Scale with Large Language Models: A Case Study
Cuevas, A., Scurrell, J. V., Brown, E. M., Entenmann, J., & Daepp, M. I. G. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710947 · Cited by 23 · see citing works
A Tree-to-Sequence Model for Neural NLG in Task-Oriented Dialog
Rao, J., Upasani, K., Balakrishnan, A., White, M., Kumar, A., & Subba, R. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8611 · Cited by 17 · see citing works
Foster, M. E., & Oberlander, J. (2010) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-010-9080-6 · Cited by 11 · see citing works
Winterboer, A. K., Tietze, M. I., Wolters, M. K., & Moore, J. D. (2010) · Computer Speech & Language · doi:10.1016/j.csl.2010.04.003 · Cited by 9 · see citing works
Self-citationCrompton, C. J., & MacPherson, S. E. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.07.006 · Cited by 8 · see citing works
Anticipating User Intentions in Customer Care Dialogue Systems
Mazzei, A., Luca Anselma, A., Sanguinetti, M., Rapp, A., Mana, D., Hossain, M. M., Patti, V., Simeoni, R., & Longo, L. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3184400 · Cited by 8 · see citing works
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented DialoguePreprint
Balakrishnan, A., Rao, J., Upasani, K., White, M., & Subba, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07220 · Cited by 8 · see citing works
Trippas, J. R. (2016) · Conference on Human Information Interaction and Retrieval · doi:10.1145/2854946.2854952 · Cited by 7 · see citing works
Conversational strategies : impact on search performance in a goal-oriented task
Dubiel, M., Halvey, M. J., Azzopardi, L. A., Anderson, D., & Daronnat, S. (2020) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 7 · see citing works
The Extended SPaRKy Restaurant Corpus: Designing a Corpus with Variable Information Density
Howcroft, D. M., Klakow, D., & Demberg, V. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1555 · Cited by 6 · see citing works
Self-citationOptimize the obvious: Automatic call flow generation
Suendermann, D., Liscombe, J., & Pieraccini, R. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5494936 · Cited by 6 · see citing works
Breen, A., Bui, H., Crouch, R., Farrell, K. R., Faubel, F., Gemello, R., Ganong, W. F., III, Haulick, T., Kaplan, R. M., Ortiz, C. L., et al. (2014) · Wiley eBooks · doi:10.1002/9781118706237.ch3 · Cited by 6 · see citing works
Take Back Control: User Privacy and Transparency Concerns in Personalized Conversational Agents
Hendrickx, I., van Waterschoot, J., Khan, A., ten Bosch, L., Catia Cucchiarini, A., & Strik, H. (2021) · University of Twente Research Information · Cited by 5 · see citing works
Data Vocalization with CiceroDB.
Trummer, I. (2019) · Conference on Innovative Data Systems Research · Cited by 5 · see citing works
Click or Type: An Analysis of Wizard’s Interaction for Future Wizard Interface Design
Janarthanam, S., Hill, R. L., Dickinson, A., & Fredriksson, M. (2014) · doi:10.3115/v1/w14-0203 · Cited by 3 · see citing works
Natural Language Generation in Dialogue Systems for Customer Care
Lascio, M. F. D., Sanguinetti, M., Luca Anselma, A., Mana, D., Mazzei, A., Patti, V., & Simeoni, R. (2020) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.8450 · Cited by 2 · see citing works
Content Selection for Explanation Requests in Customer-Care Domain.
Luca Anselma, A., Lascio, M. F. D., Mana, D., Mazzei, A., & Sanguinetti, M. (2020) · Institutional Research Information System University of Turin (University of Turin) · Cited by 2 · see citing works
Self-Training for Compositional Neural NLG in Task-Oriented Dialogue
Li, X., Stevens-Guille, S. J., Maskharashvili, A., & White, M. (2021) · International Conference on Natural Language Generation · doi:10.18653/v1/2021.inlg-1.10 · Cited by 2 · see citing works
Spina, D., Trippas, J. R., Cavedon, L., & Sanderson, M. (2015) · doi:10.1145/2802558.2814649 · Cited by 1 · see citing works
VOOL: A modular insight-based framework for vocalizing OLAP sessions
Francia, M., Gallinucci, E., Golfarelli, M., & Rizzi, S. (2024) · Information Systems · doi:10.1016/j.is.2024.102496 · Cited by 1 · see citing works
Towards Flexible, Small-Domain Surface Generation: Combining Data-Driven and Grammatical Approaches
Fischer, A., Demberg, V., & Klakow, D. (2015) · European Workshop on Natural Language Generation (ENLG) · doi:10.18653/v1/w15-4718 · Cited by 1 · see citing works
Self-citationDubiel, M. (2020) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · doi:10.48730/vgfp-ah87
- Authors
- Demberg, V., Winterboer, A., & Moore, J. D.
- Affiliations
- Saarland University (DE)
- University of Amsterdam (NL)
- University of Edinburgh (GB)
- Venue
- Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2011-09
- Volume
- 37
- Issue
- 3
- Pages
- 489-539
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W2064296938
- Semantic Scholar
- c2790139bdd2f219b9b1360652b87e3a7028a257
- Cited by
- 34
- References
- 45
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 5 · 2019: 6 · 2017: 1 · 2016: 1 · 2015: 4 · 2014: 4
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computational Linguistics (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Aeronautics and Space Administration
- Atomic Energy of Canada Limited
- Ames Research Center
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- spoken dialogue systems, data presentation, user preference modeling, option selection, content organization, user satisfaction, task completion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
