Evaluating a Food Preference Interview System that Generates Questions based on Embedding Representation of Knowledge and Topics

Abstract

With a goal of acquiring user's food preference through a conversation, this study proposes a method for selecting relevant topics and generating questions based on Freebase, a large-scale knowledge graph. To select relevant topics, we created a topic-embedding model that represents the correlation among topics. For missing entities in Freebase, knowledge completion was applied using knowledge graph embedding. We incorporated these functions into a dialogue system and conducted a user study. The results reveal that the proposed dialogue system more efficiently elicited words related to food and common nouns, and these words were highly correlated in a word embedding space.