  Human dialogues are scenario-based and appropriate responses generally relate
to the latent context knowledge entailed by the specific scenario. To enable
responses that are more meaningful and context-specific, we propose to improve
generative dialogue systems from the scenario perspective, where both dialogue
history and future conversation are taken into account to implicitly
reconstruct the scenario knowledge. More importantly, the conversation
scenarios are further internalized using imitation learning framework, where
the conventional dialogue model that has no access to future conversations is
effectively regularized by transferring the scenario knowledge contained in
hierarchical supervising signals from the scenario-based dialogue model, so
that the future conversation is not required in actual inference. Extensive
evaluations show that our approach significantly outperforms state-of-the-art
baselines on diversity and relevance, and expresses scenario-specific
knowledge.
