  We study the learning of a matching model for dialogue response selection.
Motivated by the recent finding that random negatives are often too trivial to
train a reliable model, we propose a hierarchical curriculum learning (HCL)
framework that consists of two complementary curricula: (1) corpus-level
curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model
gradually increases its ability in finding the matching clues between the
dialogue context and response. On the other hand, IC progressively strengthens
the model's ability in identifying the mismatched information between the
dialogue context and response. Empirical studies on two benchmark datasets with
three state-of-the-art matching models demonstrate that the proposed HCL
significantly improves the model performance across various evaluation metrics.
