  Dialogue systems attempt to facilitate conversations between humans and
computers, for purposes as diverse as small talk to booking a vacation. We are
here inspired by the performance of the recurrent neural network-based model
Sequicity, which when conducting a dialogue uses a sequence-to-sequence
architecture to first produce a textual representation of what is going on in
the dialogue, and in a further step use this along with database findings to
produce a reply to the user. We here propose a dialogue system based on the
Transformer architecture instead of Sequicity's RNN-based architecture, that
works similarly in an end-to-end, sequence-to-sequence fashion.
