Does Personality Matter? Expressive Generation for Dialogue Interaction

  • Marilyn A. Walker
  • Jennifer Sawyer
  • Grace Lin
  • Sam Wing
Conference paper


This paper summarizes our recent work on developing the technical capabilities needed to automatically generate dialogue utterances that express either a personality or the persona of a dramatic character. In previous work, we developed a personality-based generation engine, PERSONAGE, that produces dialogic restaurant recommendations that varied according to the speakers personality. More recently we have been exploring three issues: (1) how to coordinate verbal expression of personality or character with nonverbal expression through facial or body animation parameters; (2) whether we can express character models that we learn from film dialogue with the existing parameters of the PERSONAGE engine; and (3) whether we can show experimentally that expressive generation is useful in a range of tasks. Our long-term goal is to create off-the-shelf tools to support the creation of spoken dialogue agents with their own persona and personality, for a broad range of types of dialogue agents in task-oriented applications or in interactive stories and games.



Thanks to the organizers of IWSDS 2012 for organizing such a wonderful occasion for discussing work on dialogue systems and for inviting me to give a keynote at the workshop. This paper has benefited from their feedback.


  1. 1.
    Allport, G.W., Odbert, H.S.: Trait names: a psycho-lexical study. Psychol. Monogr. 47(1), (Whole No. 211) 171–220 (1936)Google Scholar
  2. 2.
    André, E., Rist, T., Susanne van Mulken, Klesen, M., Baldes, S.: The automated design of believable dialogues for animated presentation teams. Embodied Conversational Agents, pp. 220–255. MIT Press, Cambridge (2000)Google Scholar
  3. 3.
    Bee, N., Pollock, C., André, E., Walker, M.: Bossy or wimpy: expressing social dominance by combining gaze and linguistic behaviors. In: Intelligent Virtual Agents, pp. 265–271. Springer, New York (2010)Google Scholar
  4. 4.
    Beskow, J., Cerrato, L., Granström, B., House, D., Nordenberg, M., Nordstrand, M., Svanfeldt, G.: Expressive animated agents for affective dialogue systems. In Proceedings of the Tutorial and Research Workshop on Affective Dialogue Systems (ADS’04), pp. 301–304 (2004)Google Scholar
  5. 5.
    Beutnagel, M., Conkie, A., Schroeter, J., Stylianou, Y., Syrdal, A.: The AT&T Next-Generation Text-to-Speech System. In: Meeting of ASA/EAA/DAGA in Berlin, Germany (1999)Google Scholar
  6. 6.
    Bickmore, T.W.: Relational agents: Effecting change through human-computer relationships. PhD thesis, MIT Media Lab (2003)Google Scholar
  7. 7.
    Bickmore, T., Schulman, D.: The comforting presence of relational agents. In: CHI’06 extended abstracts on Human factors in computing systems, pp. 550–555. ACM, Montreal (2006)Google Scholar
  8. 8.
    Bouchard, T.J., McGue, M.: Genetic and environmental influences on human psychological differences. J. Neurobiol. 54, 4–45 (2003)CrossRefGoogle Scholar
  9. 9.
    Brennan, S.E.: Conversations with and through computers. User Modeling and User-Adapted Interaction 1 67–86 (1991)CrossRefGoogle Scholar
  10. 10.
    Brennan, S.E.: Lexical entrainment in spontaneous dialog. In: 1996 International Symposium on Spoken Dialogue, pp. 41–44 (1996)Google Scholar
  11. 11.
    Brennan, S.E., Clark, H.H.: Lexical choice and conceptual pacts in conversation. J. Exp. Psychol.: Learn., Mem. Cognit. 22(6), 1482–1493 (1996)Google Scholar
  12. 12.
    Cassell, J., Bickmore, T., Negotiated collusion: Modeling social language and its relationship effects in intelligent agents. User Modeling and User-Adapted Interaction 13, 89–132 (2003)CrossRefGoogle Scholar
  13. 13.
    Danlos, L: G-TAG: A lexicalized formalism for text generation inspired by tree adjoining grammar. In: Abeillé, A., Owen Rambow, O., (eds.) Tree Adjoining Grammars: Formalisms, Linguistic Analysis, and Processing. CSLI Publications (2000)Google Scholar
  14. 14.
    Eysenck, S.B.G., Eysenck, H.J., Barrett, P.: A revised version of the psychoticism scale. Personality and Individual Differences, 6(1), 21–29, (1985)CrossRefGoogle Scholar
  15. 15.
    Feng, J., Bangalore, S., Rahim, M.: Webtalk: Mining websites for automatically building dialog systems. In: Proceedings of the IEEE ASR Workshop (2003)Google Scholar
  16. 16.
    Forbes-Riley, K., Litman, D.: Investigating Human Tutor Responses to Student Uncertainty for Adaptive System Development. Lect. Notes Comput. Sci. 4738, 678–689 (2007)CrossRefGoogle Scholar
  17. 17.
    Forbes-Riley, K., Litman, D., Rotaru, M.: Responding to Student Uncertainty During Computer Tutoring: An Experimental Evaluation. Lect. Notes Comput. Sci. 5091 60–69 (2008)CrossRefGoogle Scholar
  18. 18.
    Giles, H., Coupland, N., Coupland, J.: 1. Accommodation theory: Communication, context, and consequence. Contexts of accommodation: Developments in applied sociolinguistics, p. 1, (1991)Google Scholar
  19. 19.
    Goldberg, L.R.: An alternative “description of personality”: The Big-Five factor structure. J. Per. Soc. Psychol. 59 1216–1229 (1990)Google Scholar
  20. 20.
    Hirschberg, J.: Speaking more like you: Lexical, acoustic/prosodic, and discourse entrainment in spoken dialogue systems. In: Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue, p. 128 (2008)Google Scholar
  21. 21.
    Johnston, M., Bangalore, S., Vasireddy, G., Stent, A., Ehlen, P., Walker, M., Whittaker, S., Maloor, P.: MATCH: An architecture for multimodal dialogue systems. In: Annual Meeting of the Association for Computational Linguistics, ACL (2002)Google Scholar
  22. 22.
    Kittredge, R., Korelsky, T., Rambow, O.: On the need for domain communication knowledge. Computat. Intell. 7(4), 305–314 (1991)CrossRefGoogle Scholar
  23. 23.
    Langkilde, I., Knight, K.: Generation that exploits corpus-based statistical knowledge. In: Proceedings of COLING-ACL (1998)Google Scholar
  24. 24.
    Lester, J.C., Converse, S.A., Kahler, S.E., Barlow, S.T., Stone, B.A. Bhogal, R.S.: The persona effect: affective impact of animated pedagogical agents. Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 359–366 (1997)Google Scholar
  25. 25.
    Levelt, W.J.M., Kelter, S.: Surface form and memory in question answering. Cognit. Psychol. 14, 78–106 (1982)CrossRefGoogle Scholar
  26. 26.
    Levin, E., Pieraccini, R.: A stochastic model of computer-human interaction for learning dialogue strategies. In: EUROSPEECH 97 (1997)Google Scholar
  27. 27.
    Lin, G.I., Walker, M.A.: All the world’s a stage: Learning character models from film. In: Proceedings of the Seventh AI and Interactive Digital Entertainment Conference, AIIDE ’11. AAAI (2011)Google Scholar
  28. 28.
    Litman, D.J., Pan, S.: Predicting and adapting to poor speech recognition in a spoken dialogue system. In: Proc. of the Seventeenth National Conference on Artificial Intelligence, AAAI-2000 pp. 15–21, Austin (2000)Google Scholar
  29. 29.
    Mairesse, F., Walker, M.A.: Towards personality-based user adaptation: psychologically informed stylistic language generation. User Modeling and User-Adapted Interaction 20(3), 1–52 (2010)CrossRefGoogle Scholar
  30. 30.
    Mairesse, F., Walker, M.A.: PERSONAGE: Personality generation for dialogue. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 496–503 (2007)Google Scholar
  31. 31.
    Mairesse, F., Walker, M.A.: Trainable generation of Big-Five personality styles through data-driven parameter estimation. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL) (2008)Google Scholar
  32. 32.
    Moore, J.D., Paris, C.L.: Planning text for advisory dialogues. In: Proceedings of 27th Annual Meeting of the Association for Computational Linguistics, Vancouver (1989)Google Scholar
  33. 33.
    Mott, B., Lester, J.: Narrative-centered tutorial planning for inquiry-based learning environments. In: Intelligent Tutoring Systems, pp. 675–684. Springer, New York (2006)Google Scholar
  34. 34.
    Neff, M., Toothman, N., Bowmani, R., Tree, J.E.F., and Walker, M. A.: Dont scratch! self-adaptors reflect emotional stability. In: Intelligent Virtual Agents, vol. 6895, Springer, New York (2011)Google Scholar
  35. 35.
    Neff, M., Wang, Y., Abbott, R., Walker, M.: Evaluating the effect of gesture and language on personality perception in conversational agents. In: Intelligent Virtual Agents, pp. 222–235. Springer, New York (2010)Google Scholar
  36. 36.
    Nenkova, A., Gravano, A., Hirschberg, J.: High frequency word entrainment in spoken dialogue. In: Proceedings of ACL-08: HLT. Association for Computational Linguistics (2008)Google Scholar
  37. 37.
    Niederhoffer, K.G., Pennebaker, J.W.: Linguistic style matching in social interaction. J. Lang. Soc. Psychol. 21, 337–360 (2002)CrossRefGoogle Scholar
  38. 38.
    Norman, W.T.: Toward an adequate taxonomy of personality attributes: Replicated factor structure in peer nomination personality rating. J. Abnorm. Soc. Psychol. 66, 574–583 (1963)CrossRefGoogle Scholar
  39. 39.
    Peabody, D., Goldberg, L.R.: Some determinants of factor structures from personality-trait descriptor. J. Pers. Soc. Psychol. 57(3), 552–567 (1989)CrossRefGoogle Scholar
  40. 40.
    Pickering, M., Garrod, S.: Towards a mechanistic theory of dialogue. Behav. Brain Sci. 7, 77–83 (2003)Google Scholar
  41. 41.
    Pieraccini, R., Levin, E.: A learning approach to natural language understanding. In: Speech Recognition and Coding, New Advances and Trends,NATO ASI Series, pp. 139–155. Springer, New York (1995)Google Scholar
  42. 42.
    Pieraccini, R., Levin, E., Eckert W.: AMICA: The AT&T mixed initiative conversational architecture. In: Eurospeech (1997)Google Scholar
  43. 43.
    Porayska-Pomsta, K., Mellish, C.: Modelling politeness in natural language generation. In: Proceedings of the 3rd Conference on INLG, pp. 141–150, Springer, New York (2004)Google Scholar
  44. 44.
    Rambow, O., Rogati, M., Walker, M.: Evaluating a trainable sentence planner for a spoken dialogue travel system. In: Proceedings of the Meeting of the Association for Computational Lingustics, ACL 2001 (2001)Google Scholar
  45. 45.
    Rambow, O., Korelsky, T.: Applied text generation. In: Proceedings of the Third Conference on Applied Natural Language Processing, ANLP92, pp. 40–47 (1992)Google Scholar
  46. 46.
    Reeves, B., Nass, C.: The Media Equation. University of Chicago Press, Princeton (1996)Google Scholar
  47. 47.
    Reiter, E., Dale, R.: Building Natural Language Generation Systems. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  48. 48.
    Reitter, D., Keller, F., Moore, J.D.: Computational modelling of structural priming in dialogue. Proceedings of Human Language Technology conference-North American chapter of the Association for Computational Linguistics annual mtg, New York (2006)Google Scholar
  49. 49.
    Revelle, W.: Personality processes. Annu. Rev. Psychol. 46, 295–328 (1991)CrossRefGoogle Scholar
  50. 50.
    Scheffler, K., Young, S.: Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning. In: Human Language Technology Conference (2002)Google Scholar
  51. 51.
    Scott, D.S., de Souza, C.S.: Getting the message across in RST-based text generation. In: Dale, R., Mellish, C., Zock, M. (eds.) Current Research in Natural Language Generation. Academic Press, London (1990)Google Scholar
  52. 52.
    Stenchikova, S., Stent, A.: Measuring adaptation between dialogs. Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue (2007)Google Scholar
  53. 53.
    Stent, A., Prasad, R., Walker, M.A.: Trainable sentence planning for complex information presentation in spoken dialog systems. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL) (2004)Google Scholar
  54. 54.
    Tapus, A., Tapus, C., Mataric, M.J.: User robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy. Intell. Serv. Robot. 1(2), 169–183 (2008) this is really not a very good paper.Google Scholar
  55. 55.
    Tapus, A., Mataric, M.: Socially assistive robots: The link between personality, empathy, physiological signals, and task performance. In: AAAI Spring Symposium, Palo Alto (2008)Google Scholar
  56. 56.
    Van Santen, J., Black, L., Cohen, G., Kain, A., Klabbers, E., Mishra, T., de Villiers, J., Niu, X.: Applications of computer generated expressive speech for communication disorders. In: Proceedings of Interspeech–Eurospeech, pp. 1657–1660, (2003)Google Scholar
  57. 57.
    Walker, M.A., Grant, R., Sawyer, J., Lin, G.I., Wardrip-Fruin, N., Buell, M.: Perceived or not perceived: Film character models for expressive nlg. In: International Conference on Interactive Digital Storytelling, ICIDS’11, Vancouver (2011)Google Scholar
  58. 58.
    Walker, M.A., Rambow, O., Rogati, M.: Training a sentence planner for spoken dialogue using boosting. Comput. Speech Lang.: Special Issue on Spoken Language Generation, 16(3-4), 409–433 (2002)Google Scholar
  59. 59.
    Walker, M.A., Rambow, O.: Spoken language generation. Comput. Speech Lang., Special Issue on Spoken Language Generation 16(3-4), 273–281 (2002)Google Scholar
  60. 60.
    Walker, M.A., Stent, A., Mairesse, F., Prasad, R.: Individual and domain adaptation in sentence planning for dialogue. J. Artif. Intell. Res. (JAIR) 30, 413–456 (2007)Google Scholar
  61. 61.
    Wang, N., Johnson, W.L., Mayer, R.E., Rizzo, P., Shaw, E., Collins, H.: The politeness effect: Pedagogical agents and learning gains. Front. Artif. Intell. Appl. 125, 686–693 (2005)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Marilyn A. Walker
    • 1
  • Jennifer Sawyer
    • 1
  • Grace Lin
    • 1
  • Sam Wing
    • 1
  1. 1.Natural Language and Dialogue Systems LabBaskin School of Engineering, University of CaliforniaSanta CruzUSA

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