  A typical conversation comprises of multiple turns between participants where
they go back-and-forth between different topics. At each user turn, dialogue
state tracking (DST) aims to estimate user's goal by processing the current
utterance. However, in many turns, users implicitly refer to the previous goal,
necessitating the use of relevant dialogue history. Nonetheless, distinguishing
relevant history is challenging and a popular method of using dialogue recency
for that is inefficient. We, therefore, propose a novel framework for DST that
identifies relevant historical context by referring to the past utterances
where a particular slot-value changes and uses that together with weighted
system utterance to identify the relevant context. Specifically, we use the
current user utterance and the most recent system utterance to determine the
relevance of a system utterance. Empirical analyses show that our method
improves joint goal accuracy by 2.75% and 2.36% on WoZ 2.0 and MultiWoZ 2.0
restaurant domain datasets respectively over the previous state-of-the-art GLAD
model.
