  Designing a spoken language understanding system for command-and-control
applications can be challenging because of a wide variety of domains and users
or because of a lack of training data. In this paper we discuss a system that
learns from scratch from user demonstrations. This method has the advantage
that the same system can be used for many domains and users without
modifications and that no training data is required prior to deployment. The
user is required to train the system, so for a user friendly experience it is
crucial to minimize the required amount of data. In this paper we investigate
whether a capsule network can make efficient use of the limited amount of
available training data. We compare the proposed model to an approach based on
Non-negative Matrix Factorisation which is the state-of-the-art in this setting
and another deep learning approach that was recently introduced for end-to-end
spoken language understanding. We show that the proposed model outperforms the
baseline models for three command-and-control applications: controlling a small
robot, a vocally guided card game and a home automation task.
