Evaluating the Cranfield Paradigm for Conversational Search Systems

Xiao Fu, Emine Yilmaz, Aldo Lipani

Abstract: Due to the sequential and interactive nature of conversations, the application of traditional Information Retrieval (IR) methods like the Cranfield paradigm require stronger assumptions. When building a test collection for Ad Hoc search, it is fair to assume that the relevance judgments provided by an annotator correlate well with the relevance judgments perceived by an actual user of the search engine. However, when building a test collection for conversational search, we do not know if it is fair to assume the same. In this paper, we perform a crowdsourcing study to evaluate the applicability of the Cranfield paradigm to conversational search systems. Our main aim is to understand what is the agreement in terms of user satisfaction between the users performing a search task in a conversational search system (i.e., directly assessing the system) and the users observing the search task being performed (i.e., indirectly assessing the system). The results of this study are paramount because they underpin and guide 1) the development of more realistic user models and simulators, and 2) the design of more reliable and robust evaluation measures for conversational search systems. Our results show that there is a fair agreement between direct and indirect assessments in terms of user satisfaction and that these two kinds of assessments share similar conversational patterns. Indeed, by collecting relevance assessments for each system utterance, we tested several conversational patterns that show a promising ability to predict user satisfaction.