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Most dialog systems posit that users have figured out clear and specificgoals before starting an interaction. For example, users have determined thedeparture, the destination, and the travel time for booking a flight. However,in many scenarios, limited by experience and knowledge, users may know whatthey need, but still struggle to figure out clear and specific goals bydetermining all the necessary slots.  In this paper, we identify this challenge and make a step forward bycollecting a new human-to-human mixed-type dialog corpus. It contains 5k dialogsessions and 168k utterances for 4 dialog types and 5 domains. Within eachsession, an agent first provides user-goal-related knowledge to help figure outclear and specific goals, and then help achieve them.  Furthermore, we propose a mixed-type dialog model with a novel Prompt-basedcontinual learning mechanism. Specifically, the mechanism enables the model tocontinually strengthen its ability on any specific type by utilizing existingdialog corpora effectively.

Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals