Towards Intent-Driven Transparency in Conversational Search Systems

Published: 01 Jan 2025, Last Modified: 18 May 2025ECIR (5) 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: In the field of Information Retrieval (IR), understanding user queries is a key area of focus, centered on identifying the underlying intent behind a search input. Many current methods simplify this task to query classification or clustering, which often fails to capture the subtle nuances in user intent. Conversational search is an evolving paradigm for next-generation search engines, using natural language dialogue to enhance complex and precise information retrieval—a feature that has drawn considerable interest. Unlike traditional search, conversational search involves multi-turn dialogues, which bring additional challenges such as ambiguous or incomplete queries, often caused by references to previous messages, and the omission of prior context. The need to dynamically maintain awareness of the historical turns and handle potential shifts in user intent adds complexity to intent analysis. As a result, achieving a context-aware understanding of query intent within conversational search systems is a significant challenge. This work aims to establish intent-driven transparency in these systems, paving the way for more intuitive and responsive conversational search experiences.
Loading