让用户通过可视化交互逐步澄清模糊查询，提升自然语言数据库查询的准确性。
PleaSQLarify: Visual Pragmatic Repair for Natural Language Database Querying
- 基于语用推理设计交互机制，以最小代价逐步澄清用户意图。
- 12名参与者测试显示，系统能有效帮助用户识别多种解释并快速纠错。
- 适合需要高精度查询的场景，如数据分析、科研人员使用数据库时。
自然语言数据库接口虽拓展了数据访问范围，但在输入模糊时仍易出错。传统方法常将不确定性压缩为单一查询，难以应对用户意图与系统理解之间的偏差。本文提出 extsc{PleaSQLarify}，从语用推理角度重构问题：用户倾向于简化表达，而系统基于动作空间先验，二者可能不一致。因此，通过最小交互实现增量式澄清（即语用修复）是合理策略。该系统围绕可解释的决策变量组织交互，配合可视化界面，展示可能的动作空间，引导用户澄清，并追踪各轮对话中的信念更新。在12名参与者的实验中， extsc{PleaSQLarify} 帮助用户识别多种可能解释，并高效解决歧义。研究结果表明，语用修复是一种促进用户控制的有效设计原则。
原文摘要 · Abstract (English)
Natural language database interfaces broaden data access, yet they remain brittle under input ambiguity. Standard approaches often collapse uncertainty into a single query, offering little support for mismatches between user intent and system interpretation. We reframe this challenge through pragmatic inference: while users economize expressions, systems operate on priors over the action space that may not align with the users'. In this view, pragmatic repair -- incremental clarification through minimal interaction -- is a natural strategy for resolving underspecification. We present \textsc{PleaSQLarify}, which operationalizes pragmatic repair by structuring interaction around interpretable decision variables that enable efficient clarification. A visual interface complements this by surfacing the action space for exploration, requesting user disambiguation, and making belief updates traceable across turns. In a study with twelve participants, \textsc{PleaSQLarify} helped users recognize alternative interpretations and efficiently resolve ambiguity. Our findings highlight pragmatic repair as a design principle that fosters effective user control in natural language interfaces.
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