Dialogue systems are usually categorized into two types, open-domain and
task-oriented. The first one focuses on chatting with users and making them
engage in the conversations, where selecting a proper topic to fit the dialogue
context is essential for a successful dialogue. The other one focuses on a
specific task instead of casual talks, e.g., finding a movie on Friday night,
or playing a song. These two directions have been studied separately due to
their different purposes. However, how smoothly transitioning from social
chatting to task-oriented dialogues is important for triggering business
opportunities, and there is no public data focusing on such scenarios. Hence,
this paper focuses on investigating the conversations starting from open-domain
social chatting and then gradually transitioning to task-oriented purposes, and
releases a large-scale dataset with detailed annotations for encouraging this
research direction. To achieve this goal, this paper proposes a framework to
automatically generate many dialogues without human involvement, in which any
powerful open-domain dialogue generation model can be easily leveraged. The
human evaluation shows that our generated dialogue data has a natural flow at a
reasonable quality, showing that our released data has a great potential of
guiding future research directions and commercial activities. Furthermore, the
released models allow researchers to automatically generate unlimited dialogues
in the target scenarios, which can greatly benefit semi-supervised and
unsupervised approaches.

探索如何从社交对话到任务导向对话的平滑转换，为触发商业机会提供支持。提出了一个自动生成对话的框架，并在此基础上发布了一个大规模数据集，该数据集为未来的研究和商业活动提供了巨大的潜力。