  The latency in the current neural based dialogue state tracking models
prohibits them from being used efficiently for deployment in production
systems, albeit their highly accurate performance. This paper proposes a new
scalable and accurate neural dialogue state tracking model, based on the
recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et
al. which uses global modules to share parameters between estimators for
different types (called slots) of dialogue states, and uses local modules to
learn slot-specific features. By using only one recurrent networks with global
conditioning, compared to (1 + \# slots) recurrent networks with global and
local conditioning used in the GLAD model, our proposed model reduces the
latency in training and inference times by $35\%$ on average, while preserving
performance of belief state tracking, by $97.38\%$ on turn request and
$88.51\%$ on joint goal and accuracy. Evaluation on Multi-domain dataset
(Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform
and joint goal accuracy.
