Increasing the Coverage of Clarification Responses for a Cooking Assistant

Abstract

In conversation genres like instruction, clarification questions asked by a user may either relate to the task at hand or to common-sense knowledge about the task domain, whereas most conversational agents focus on only one of these types. To learn more about the best approach and feasibility of integrating both types of questions, we experimented with different approaches for modelling and distinguishing between task-specific and common sense questions in the context of a cooking assistant. We subsequently integrated the best ones in a conversational agent, which we tested in a study with six users cooking a recipe. Even though the three elements functioned well on their own and all participants completed the recipe, question-answering accuracy was relatively low (66%). We conclude with a discussion of the aspects that need to be improved upon to cope with the diverse information need in task-based conversational agents.