User Intention Recognition and Requirement Elicitation Method for Conversational AI Services

TL;DR

This paper introduces a novel approach for user intention recognition and requirement elicitation in conversational AI, utilizing Knowledge Graphs and Granular Computing to improve accuracy and reduce dialogue rounds.

Contribution

It proposes new methods based on Knowledge Graphs and Granular Computing for more efficient and accurate user requirement elicitation in chatbots.

Findings

Reduces number of conversation rounds

Quickly and accurately identifies user intentions

Improves user experience in chatbot interactions

Abstract

In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement expression caused by the information asymmetry, the other is that the diversity of service resources leads to the difficulty of service selection. Conversational bot is a typical mesh device, so the guided multi-rounds Q$\&$A is the most effective way to elicit user requirements. Obviously, complex Q$\&$A with too many rounds is boring and always leads to bad user experience. Therefore, we aim to obtain user requirements as accurately as possible in as few rounds as possible. To achieve this, a user intention recognition method based on Knowledge Graph (KG) was developed for fuzzy requirement inference, and a requirement elicitation method based on Granular Computing was proposed for dialog policy generation. Experimental results show that these two methods can effectively reduce the number of conversation rounds, and can quickly and accurately identify the user intention.