  Dialogue state tracking (DST) aims to predict the current dialogue state
given the dialogue history. Existing methods generally exploit the utterances
of all dialogue turns to assign value for each slot. This could lead to
suboptimal results due to the information introduced from irrelevant utterances
in the dialogue history, which may be useless and can even cause confusion. To
address this problem, we propose LUNA, a sLot-tUrN Alignment enhanced approach.
It first explicitly aligns each slot with its most relevant utterance, then
further predicts the corresponding value based on this aligned utterance
instead of all dialogue utterances. Furthermore, we design a slot ranking
auxiliary task to learn the temporal correlation among slots which could
facilitate the alignment. Comprehensive experiments are conducted on
multi-domain task-oriented dialogue datasets, i.e., MultiWOZ 2.0, MultiWOZ 2.1,
and MultiWOZ 2.2. The results show that LUNA achieves new state-of-the-art
results on these datasets.
