  Existing approaches to Dialogue State Tracking (DST) rely on turn level
dialogue state annotations, which are expensive to acquire in large scale. In
call centers, for tasks like managing bookings or subscriptions, the user goal
can be associated with actions (e.g.~API calls) issued by customer service
agents. These action logs are available in large volumes and can be utilized
for learning dialogue states. However, unlike turn-level annotations, such
logged actions are only available sparsely across the dialogue, providing only
a form of weak supervision for DST models. To efficiently learn DST with sparse
labels, we extend a state-of-the-art encoder-decoder model. The model learns a
slot-aware representation of dialogue history, which focuses on relevant turns
to guide the decoder. We present results on two public multi-domain DST
datasets (MultiWOZ and Schema Guided Dialogue) in both settings i.e. training
with turn-level and with sparse supervision. The proposed approach improves
over baseline in both settings. More importantly, our model trained with sparse
supervision is competitive in performance to fully supervised baselines, while
being more data and cost efficient.
