  Recently, researchers have explored using the encoder-decoder framework to
tackle dialogue state tracking (DST), which is a key component of task-oriented
dialogue systems. However, they regard a multi-turn dialogue as a flat
sequence, failing to focus on useful information when the sequence is long. In
this paper, we propose a Hierarchical Dynamic Copy Network (HDCN) to facilitate
focusing on the most informative turn, making it easier to extract slot values
from the dialogue context. Based on the encoder-decoder framework, we adopt a
hierarchical copy approach that calculates two levels of attention at the word-
and turn-level, which are then renormalized to obtain the final copy
distribution. A focus loss term is employed to encourage the model to assign
the highest turn-level attention weight to the most informative turn.
Experimental results show that our model achieves 46.76% joint accuracy on the
MultiWOZ 2.1 dataset.
