Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems

Understand

Domain Adaptation arises when we aim at learning from source domain a model that can per- form acceptably well on a different target domain.

• It is especially crucial for Natural Language Generation (NLG) in Spoken Dialogue Systems when there are sufficient annotated data in the source domain, but there is a limited labeled data in the target domain.
• How to effectively utilize as much of existing abilities from source domains is a crucial issue in domain adaptation.
• In this paper, we propose an adversarial training procedure to train a Variational encoder-decoder based language generator via multiple adaptation steps.