  Hierarchical neural networks are often used to model inherent structures
within dialogues. For goal-oriented dialogues, these models miss a mechanism
adhering to the goals and neglect the distinct conversational patterns between
two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical
Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture
interlocutor-level disparity while modeling goal-oriented dialogues.
Experiments on dialogue generation, response generation, and human evaluations
demonstrate that the proposed model successfully generates higher-quality, more
diverse and goal-centric dialogues. Moreover, we apply data augmentation via
goal-oriented dialogue generation for task-oriented dialog systems with better
performance achieved.
