  Dialogue state tracking is an important component in task-oriented dialogue
systems to identify users' goals and requests as a dialogue proceeds. However,
as most previous models are dependent on dialogue slots, the model complexity
soars when the number of slots increases. In this paper, we put forward a
slot-independent neural model (SIM) to track dialogue states while keeping the
model complexity invariant to the number of dialogue slots. The model utilizes
attention mechanisms between user utterance and system actions. SIM achieves
state-of-the-art results on WoZ and DSTC2 tasks, with only 20% of the model
size of previous models.
