Ericson: An Interactive Open-Domain Conversational Search Agent
This work addresses the problem of providing up-to-date information through natural conversations for users, but it is incremental as it builds on existing techniques with empirical validation.
The paper tackled the challenge of building an effective open-domain conversational search agent by presenting Ericson, a system that integrated state-of-the-art components and was tested in live conversations with thousands of users, revealing that accurate intent classification, user engagement, and proactive recommendations were key to satisfaction.
Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs. However, creating an effective and robust ODCS agent is challenging. In this paper, we present a fully functional ODCS system, Ericson, which includes state-of-the-art question answering and information retrieval components, as well as intent inference and dialogue management models for proactive question refinement and recommendations. Our system was stress-tested in the Amazon Alexa Prize, by engaging in live conversations with thousands of Alexa users, thus providing empirical basis for the analysis of the ODCS system in real settings. Our interaction data analysis revealed that accurate intent classification, encouraging user engagement, and careful proactive recommendations contribute most to the users satisfaction. Our study further identifies limitations of the existing search techniques, and can serve as a building block for the next generation of ODCS agents.