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:公开领域对话搜索(ODCS)旨在提供有价值的最新信息,同时通过保持自然的对话方式帮助用户细化并最终回答信息需求。然而,创建一个有效且健壮的ODCS代理是具有挑战性的。在本文中,我们介绍了一种完全功能的ODCS系统Ericson,它包括最先进的问答和信息检索组件,以及用于积极问题细化和推荐的意图推断和对话管理模型。通过在亚马逊Alexa奖项中进行压力测试,并与数千个Alexa用户进行实时对话,从而为分析ODCS系统在真实环境中的表现提供了经验基础。我们的交互数据分析显示,准确的意图分类、鼓励用户参与以及谨慎的积极推荐对用户的满意度做出了最大的贡献。我们的研究进一步确定了现有搜索技术的限制,并可作为下一代ODCS代理的构建模块。