  With the flourish of services on the Internet, a prerequisite for service
providers to precisely deliver services to their customers is to capture user
requirements comprehensively, accurately, and efficiently. This is called the
``Service Requirement Elicitation (SRE)'' task. Considering the amount of
customers is huge, it is an inefficient way for service providers to interact
with each user by face-to-face dialog. Therefore, to elicit user requirements
with the assistance of virtual intelligent assistants has become a mainstream
way. Since user requirements generally consist of different levels of details
and need to be satisfied by services from multiple domains, there is a huge
potential requirement space for SRE to explore to elicit complete requirements.
Considering that traditional dialogue system with static slots cannot be
directly applied to the SRE task, it is a challenge to design an efficient
dialogue strategy to guide users to express their complete and accurate
requirements in such a huge potential requirement space. Based on the
phenomenon that users tend to express requirements subjectively in a sequential
manner, we propose a Personalized Utterance Style (PUS) module to perceive the
personalized requirement expression habits, and then apply PUS to an dialogue
strategy to efficiently complete the SRE task. Specifically, the dialogue
strategy chooses suitable response actions for dynamically updating the
dialogue state. With the assistance of PUS extracted from dialogue history, the
system can shrink the search scope of potential requirement space. Experiment
results show that the dialogue strategy with PUS can elicit more accurate user
requirements with fewer dialogue rounds.
