  In recent research on dialogue systems and corpora, there has been a
significant focus on two distinct categories: task-oriented (TOD) and
open-domain (chit-chat) dialogues. TOD systems aim to satisfy specific user
goals, such as finding a movie to watch, whereas open-domain systems primarily
focus on generating engaging conversations. A recent study by Chiu et al.
(2022) introduced SalesBot, which provides simulators and a dataset with
one-turn transition from chit-chat to task-oriented dialogues. However, the
previously generated data solely relied on BlenderBot, which raised concerns
about its long-turn naturalness and consistency during a conversation. To
address this issue, this paper aims to build SalesBot 2.0, a revised version of
the published data, by leveraging the commonsense knowledge of large language
models (LLMs) through proper prompting. The objective is to gradually bridge
the gap between chit-chat and TOD towards better naturalness and consistency.
The newly released large-scale dataset with detailed annotations exhibits
smoother transitions between topics and is more human-like in terms of
naturalness and consistency. It can serve as a valuable resource for both
academic research and commercial applications. Furthermore, our proposed
framework can be applied to generate numerous dialogues with various target
intents.
