搜索中基于Facet的澄清提示生成的一致性分析
Analyzing Coherency in Facet-based Clarification Prompt Generation for Search
AI总结：
本文聚焦搜索澄清中的查询facet质量，提出facet连贯性概念并发现现有评估未涵盖该指标，同时构建分类器揭示了不连贯facet的普遍性，为未来研究提供动机。
摘要：
澄清用户的信息需求是现代搜索系统的核心组件。尽管大多数构建澄清提示的方法依赖于查询facet，但facet质量的影响却鲜有探讨。本研究聚焦于facet质量，通过facet连贯性这一概念，评估其对搜索澄清整体有用性的重要性。我们发现现有评估流程未考虑facet连贯性，这从连贯性与自动评估指标之间极差的相关性中得以印证。此外，我们提出了一种连贯性分类器，并在一个成熟的澄清数据集中评估了不连贯facet的普遍程度。我们的研究结果可为该主题的未来研究提供动机。
英文摘要：
Clarifying user's information needs is an essential component of modern search systems. While most of the approaches for constructing clarifying prompts rely on query facets, the impact of the quality of the facets is relatively unexplored. In this work, we concentrate on facet quality through the notion of facet coherency and assess its importance for overall usefulness for clarification in search. We find that existing evaluation procedures do not account for facet coherency, as evident by the poor correlation of coherency with automated metrics. Moreover, we propose a coherency classifier and assess the prevalence of incoherent facets in a well-established dataset on clarification. Our findings can serve as motivation for future work on the topic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
