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Conversations have an intrinsic one-to-many property, which means thatmultiple responses can be appropriate for the same dialog context. Intask-oriented dialogs, this property leads to different valid dialog policiestowards task completion. However, none of the existing task-oriented dialoggeneration approaches takes this property into account. We propose aMulti-Action Data Augmentation (MADA) framework to utilize the one-to-manyproperty to generate diverse appropriate dialog responses. Specifically, wefirst use dialog states to summarize the dialog history, and then discover allpossible mappings from every dialog state to its different valid systemactions. During dialog system training, we enable the current dialog state tomap to all valid system actions discovered in the previous process to createadditional state-action pairs. By incorporating these additional pairs, thedialog policy learns a balanced action distribution, which further guides thedialog model to generate diverse responses. Experimental results show that theproposed framework consistently improves dialog policy diversity, and resultsin improved response diversity and appropriateness. Our model obtainsstate-of-the-art results on MultiWOZ.

Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context