  In dialogue state tracking, dialogue history is a crucial material, and its
utilization varies between different models. However, no matter how the
dialogue history is used, each existing model uses its own consistent dialogue
history during the entire state tracking process, regardless of which slot is
updated. Apparently, it requires different dialogue history to update different
slots in different turns. Therefore, using consistent dialogue contents may
lead to insufficient or redundant information for different slots, which
affects the overall performance. To address this problem, we devise DiCoS-DST
to dynamically select the relevant dialogue contents corresponding to each slot
for state updating. Specifically, it first retrieves turn-level utterances of
dialogue history and evaluates their relevance to the slot from a combination
of three perspectives: (1) its explicit connection to the slot name; (2) its
relevance to the current turn dialogue; (3) Implicit Mention Oriented
Reasoning. Then these perspectives are combined to yield a decision, and only
the selected dialogue contents are fed into State Generator, which explicitly
minimizes the distracting information passed to the downstream state
prediction. Experimental results show that our approach achieves new
state-of-the-art performance on MultiWOZ 2.1 and MultiWOZ 2.2, and achieves
superior performance on multiple mainstream benchmark datasets (including
Sim-M, Sim-R, and DSTC2).
