SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues
- URL: http://arxiv.org/abs/2204.10591v1
- Date: Fri, 22 Apr 2022 09:31:13 GMT
- Title: SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues
- Authors: Ssu Chiu, Maolin Li, Yen-Ting Lin, Yun-Nung Chen
- Abstract summary: How smoothly transitioning from social chatting to task-oriented dialogues is important for triggering business opportunities.
This paper proposes a framework to automatically generate many dialogues without human involvement.
The released data has a great potential of guiding future research directions and commercial activities.
- Score: 22.89699254073016
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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