我们问对问题吗？关于表格数据分析中自然语言查询的模糊性
Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis
AI总结：
本文探讨了自然语言查询中模糊性的合作交互机制，提出基于共享责任的框架，用于设计和评估表格数据分析的自然语言接口。
摘要：
自然语言接口处理表格数据的查询必须处理查询中固有的模糊性。与其将模糊性视为缺陷，我们将其重新界定为合作交互的一种特征，用户在查询的明确程度上是有意的。我们开发了一个基于用户和系统之间查询规范共享责任的框架，区分明确和模糊的合作查询，这些查询系统可以通过合理推断来解决，与无法解决的不合作查询相区分。将该框架应用于表格问题回答和分析的评估中，我们分析了15个数据集中的查询，并观察到查询类型之间不受控制的混合，这既不足够评估系统准确性，也不足以评估解释能力。围绕解决查询的合作概念，指导了如何设计和评估表格数据分析的自然语言接口，为此我们提炼出未来研究的具体方向和更广泛的影响。
英文摘要：
Natural language interfaces to tabular data must handle ambiguities inherent to queries. Instead of treating ambiguity as a deficiency, we reframe it as a feature of cooperative interaction where users are intentional about the degree to which they specify queries. We develop a principled framework based on a shared responsibility of query specification between user and system, distinguishing unambiguous and ambiguous cooperative queries, which systems can resolve through reasonable inference, from uncooperative queries that cannot be resolved. Applying the framework to evaluations for tabular question answering and analysis, we analyze queries in 15 datasets, and observe an uncontrolled mixing of query types neither adequate for evaluating a system's accuracy nor for evaluating interpretation capabilities. This conceptualization around cooperation in resolving queries informs how to design and evaluate natural language interfaces for tabular data analysis, for which we distill concrete directions for future research and broader implications.
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。
