  This paper presents a hybrid dialog state tracker that combines a rule based
and a machine learning based approach to belief state tracking. Therefore, we
call it a hybrid tracker. The machine learning in our tracker is realized by a
Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker
sets a new state-of-the-art result for the Dialog State Tracking Challenge
(DSTC) 2 dataset when the system uses only live SLU as its input.
