BITT: A Corpus for Topic Tracking Evaluation on Multimodal Human-Robot-Interaction

Abstract

Our research is concerned with the development of robotic systems which can support people in household environments, such as taking care of elderly people. A central goal of our research consists in creating robot systems which are able to learn and communicate about a given environment without the need of a specially trained user. For the communication with such users it is necessary that the robot is able to communicate multimodally, which especially includes the ability to communicate in natural language. Our research is concerned with the development of robotic systems which can support people in household environments, such as taking care of elderly people. A central goal of our research consists in creating robot systems which are able to learn and communicate about a given environment without the need of a specially trained user. For the communication with such users it is necessary that the robot is able to communicate multimodally, which especially includes the ability to communicate in natural language. We believe that the ability to communicate naturally in multimodal communication must be supported by the ability to access contextual information, with topical knowledge being an important aspect of this knowledge. Therefore, we currently develop a topic tracking system for situated human-robot communication on our robot systems. This paper describes the BITT (Bielefeld Topic Tracking) corpus which we built in order to develop and evaluate our system. The corpus consists of human-robot communication sequences about a home-like environment, delivering access to the information sources a multimodal topic tracking system requires.

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