  Dialogue state tracking (DST) is a pivotal component in task-oriented
dialogue systems. While it is relatively easy for a DST model to capture belief
states in short conversations, the task of DST becomes more challenging as the
length of a dialogue increases due to the injection of more distracting
contexts. In this paper, we aim to improve the overall performance of DST with
a special focus on handling longer dialogues. We tackle this problem from three
perspectives: 1) A model designed to enable hierarchical slot status
prediction; 2) Balanced training procedure for generic and task-specific
language understanding; 3) Data perturbation which enhances the model's ability
in handling longer conversations. We conduct experiments on the MultiWOZ
benchmark, and demonstrate the effectiveness of each component via a set of
ablation tests, especially on longer conversations.
