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Human dialogues are scenario-based and appropriate responses generally relateto the latent context knowledge entailed by the specific scenario. To enableresponses that are more meaningful and context-specific, we propose to improvegenerative dialogue systems from the scenario perspective, where both dialoguehistory and future conversation are taken into account to implicitlyreconstruct the scenario knowledge. More importantly, the conversationscenarios are further internalized using imitation learning framework, wherethe conventional dialogue model that has no access to future conversations iseffectively regularized by transferring the scenario knowledge contained inhierarchical supervising signals from the scenario-based dialogue model, sothat the future conversation is not required in actual inference. Extensiveevaluations show that our approach significantly outperforms state-of-the-artbaselines on diversity and relevance, and expresses scenario-specificknowledge.

Regularizing Dialogue Generation by Imitating Implicit Scenarios