How to Approach Ambiguous Queries in Conversational Search? A Survey of Techniques, Approaches, Tools and Challenges

Abstract

The advent of recent Natural Language Processing technology has led human and machine interactions more towards conversation. In Conversational Search Systems (CSS) like chatbots and Virtual Personal Assistants (VPA) such as Apple’s Siri, Amazon Alexa, Microsoft’s Cortana, and Google Assistant both user and device have limited platform to communicate through chatting or voice. In the information seeking process, often users do not know how to properly describe their information need in a machine understandable language. Consequently, it is hard for the assistant agent to predict the user’s intent and yield relevant results by only relying on the original query. Study has shown many unsatisfactory results can be enhanced with the benefit of CSS. Conversational search systems can dig deeper into the user’s query to reveal the real need. This survey intends to provide a comprehensive and comparative overview of ambiguous query clarification task in the context of conversational search technology. We investigate different approaches, their evaluation methods, and future work. We also address the importance of understanding a query for retrieving the most relevant document(s) and satisfying user’s need by predicting their potential request. This work provides a divine overview of characteristics of ambiguous queries and contributes to better understanding of the existing technologies and challenges in CSS focus on disambiguation of unclear queries from various dimensions.