PleaSQLarify: 自然语言数据库查询中的视觉语用修复
PleaSQLarify: Visual Pragmatic Repair for Natural Language Database Querying
AI总结：
PleaSQLarify通过视觉交互和可解释决策变量实现自然语言数据库查询中的语用修复，提升用户对歧义的控制与解决效率。
摘要：
自然语言数据库接口扩大了数据访问范围，但其在输入模糊性下的鲁棒性仍不足。标准方法往往将不确定性合并为一个查询，对用户意图与系统解读之间的不匹配提供很少支持。我们通过语用推理重新审视这一挑战：尽管用户在表达上节省资源，系统却基于可能与用户不一致的动作先验进行操作。在这种观点下，语用修复——通过最小交互逐步澄清——是解决不充分性的一种自然策略。我们提出了PleaSQLarify，通过围绕可解释的决策变量结构化交互，将语用修复制度化。一个视觉界面通过揭示动作空间供探索、请求用户歧义消除以及使信念更新在回合间可追溯。在十二名参与者的研究中，PleaSQLarify帮助用户识别替代解释并高效解决歧义。我们的发现强调语用修复作为一种设计原则，有助于在自然语言接口中促进有效的用户控制。
英文摘要：
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.
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。
