University of Cambridge > Talks.cam > NLIP Seminar Series > Learning to Adapt in Dialogue Systems: Data-driven Models for Personality Recognition and Generation

Learning to Adapt in Dialogue Systems: Data-driven Models for Personality Recognition and Generation

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  • UserFrancois Mairesse, Department of Engineering, University of Cambridge
  • ClockFriday 21 November 2008, 12:15-13:00
  • HouseSW01, Computer Laboratory.

If you have a question about this talk, please contact Johanna Geiss.

Most dialogue systems do not take linguistic variation into account in both the understanding and generation phases, i.e. the user’s linguistic style is typically ignored, and the style conveyed by the system is chosen once for all interactions at development time. We believe that modelling linguistic variation can greatly improve the interaction in dialogue systems, such as in intelligent tutoring systems, video games, or information retrieval systems, which all require specific linguistic styles. Previous work has shown that linguistic style affects many aspects of users’ perceptions, even when the dialogue is task-oriented. Moreover, users attribute a consistent personality to machines, even when exposed to a limited set of cues, thus dialogue systems manifest personality whether designed into the system or not.

Over the past few years, psychologists have identified the main dimensions of individual differences in human behaviour: the Big Five personality traits. We hypothesise that the Big Five provide a useful computational framework for modelling important aspects of linguistic variation. We explore the possibility of recognising the user’s personality using data-driven models trained on essays and conversational data. We then test whether it is possible to generate language varying consistently along each personality dimension in the information presentation domain. We present PERSONAGE : a language generator modelling findings from psychological studies to project various personality traits. We use PERSONAGE to compare various generation paradigms: (1) rule-based generation, (2) overgenerate and select and (3) generation using parameter estimation models—-a novel approach that learns to produce recognisable variation along meaningful stylistic dimensions without the computational cost incurred by overgeneration techniques. We also present the first human evaluation of a data-driven generation method that projects multiple stylistic dimensions simultaneously and on a continuous scale.

This talk is part of the NLIP Seminar Series series.

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