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.

翻译：澄清用户信息需求是现代搜索系统的重要组成部分。尽管大多数构建澄清提示的方法依赖于查询面片，但面片质量的影响相对而言尚未得到充分探索。本研究聚焦于面片质量，通过面片连贯性的概念，评估其对搜索澄清任务整体有效性的重要程度。我们发现现有评估程序未考虑面片连贯性，这体现在连贯性与自动化指标之间的相关性较弱。此外，我们提出了一种连贯性分类器，并评估了一个成熟的澄清数据集中不连贯面片的普遍程度。研究结果可为该领域的后续工作提供启发。