A Personalized Utterance Style (PUS) based Dialogue Strategy for Efficient Service Requirement Elicitation

TL;DR

This paper introduces a Personalized Utterance Style (PUS) module integrated into a dialogue strategy to efficiently and accurately elicit user requirements for services, reducing dialogue rounds and improving requirement completeness.

Contribution

It proposes a novel PUS module that captures user expression habits and applies it to a dialogue strategy for service requirement elicitation, addressing limitations of traditional static-slot systems.

Findings

PUS-enhanced strategy elicits more accurate requirements

Fewer dialogue rounds needed with PUS

Improved coverage of user requirements

Abstract

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.