  We study the learning of a matching model for dialogue response selection.
Motivated by the recent finding that models trained with random negative
samples are not ideal in real-world scenarios, we propose a hierarchical
curriculum learning framework that trains the matching model in an
"easy-to-difficult" scheme. Our learning framework 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 a
response candidate. As for IC, it progressively strengthens the model's ability
in identifying the mismatching information between the dialogue context and a
response candidate. Empirical studies on three benchmark datasets with three
state-of-the-art matching models demonstrate that the proposed learning
framework significantly improves the model performance across various
evaluation metrics.
