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

本文提出了一个基于人类混合类型对话语料库的混合式对话模型，其中包含 5k 对话会话和 168k 话语，用于帮助确定用户目标并实现目标，并提供了基于提示的持续学习机制，以帮助模型不断提高其在任何特定类型上的能力。