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.

翻译：开放域对话式搜索（ODCS）旨在提供有价值且最新的信息，同时保持自然对话，以帮助用户细化并最终满足信息需求。然而，构建一个有效且稳健的ODCS代理颇具挑战性。本文介绍了一个功能完备的ODCS系统——Ericson，它集成了最先进的问答与信息检索组件，以及用于主动问题细化与推荐的意图推理与对话管理模型。该系统通过Amazon Alexa Prize活动，与数千名Alexa用户进行实时对话，经受住了压力测试，从而为真实环境中ODCS系统的分析提供了实证基础。我们的交互数据分析表明，准确的意图分类、鼓励用户参与以及谨慎的主动推荐对用户满意度贡献最大。本研究进一步揭示了现有搜索技术的局限性，可为下一代ODCS代理的开发奠定基础。