  Existing dialog state tracking (DST) models are trained with dialog data in a
random order, neglecting rich structural information in a dataset. In this
paper, we propose to use curriculum learning (CL) to better leverage both the
curriculum structure and schema structure for task-oriented dialogs.
Specifically, we propose a model-agnostic framework called Schema-aware
Curriculum Learning for Dialog State Tracking (SaCLog), which consists of a
preview module that pre-trains a DST model with schema information, a
curriculum module that optimizes the model with CL, and a review module that
augments mispredicted data to reinforce the CL training. We show that our
proposed approach improves DST performance over both a transformer-based and
RNN-based DST model (TripPy and TRADE) and achieves new state-of-the-art
results on WOZ2.0 and MultiWOZ2.1.
