Matthias Kraus


ProDial – An Annotated Proactive Dialogue Act Corpus for Conversational Assistants using Crowdsourcing
Matthias Kraus | Nicolas Wagner | Wolfgang Minker
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Robots will eventually enter our daily lives and assist with a variety of tasks. Especially in the household domain, robots may become indispensable helpers by overtaking tedious tasks, e.g. keeping the place tidy. Their effectiveness and efficiency, however, depend on their ability to adapt to our needs, routines, and personal characteristics. Otherwise, they may not be accepted and trusted in our private domain. For enabling adaptation, the interaction between a human and a robot needs to be personalized. Therefore, the robot needs to collect personal information from the user. However, it is unclear how such sensitive data can be collected in an understandable way without losing a user’s trust in the system. In this paper, we present a conversational approach for explicitly collecting personal user information using natural dialogue. For creating a sound interactive personalization, we have developed an empathy-augmented dialogue strategy. In an online study, the empathy-augmented strategy was compared to a baseline dialogue strategy for interactive personalization. We have found the empathy-augmented strategy to perform notably friendlier. Overall, using dialogue for interactive personalization has generally shown positive user reception.


A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation
Matthias Kraus | Fabian Fischbach | Pascal Jansen | Wolfgang Minker
Proceedings of the Twelfth Language Resources and Evaluation Conference

Recommendation systems aim at facilitating information retrieval for users by taking into account their preferences. Based on previous user behaviour, such a system suggests items or provides information that a user might like or find useful. Nonetheless, how to provide suggestions is still an open question. Depending on the way a recommendation is communicated influences the user’s perception of the system. This paper presents an empirical study on the effects of proactive dialogue strategies on user acceptance. Therefore, an explicit strategy based on user preferences provided directly by the user, and an implicit proactive strategy, using autonomously gathered information, are compared. The results show that proactive dialogue systems significantly affect the perception of human-computer interaction. Although no significant differences are found between implicit and explicit strategies, proactivity significantly influences the user experience compared to reactive system behaviour. The study contributes new insights to the human-agent interaction and the voice user interface design. Furthermore, we discover interesting tendencies that motivate futurework.


Effects of Gender Stereotypes on Trust and Likability in Spoken Human-Robot Interaction
Matthias Kraus | Johannes Kraus | Martin Baumann | Wolfgang Minker
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


Quality-adaptive Spoken Dialogue Initiative Selection And Implications On Reward Modelling
Stefan Ultes | Matthias Kraus | Alexander Schmitt | Wolfgang Minker
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue