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 the responsibility of query specification is shared among the user and the system. We develop a principled framework distinguishing cooperative queries, i.e., queries that yield a resolvable interpretation, from uncooperative queries that cannot be resolved. Applying the framework to evaluations for tabular question answering and analysis, we analyze the queries in 15 popular datasets, and observe an uncontrolled mixing of query types neither adequate for evaluating a system's execution accuracy nor for evaluating interpretation capabilities. Our framework and analysis of queries shifts the perspective from fixing ambiguity to embracing cooperation in resolving queries. This reflection enables more informed design and evaluation for natural language interfaces for tabular data, for which we outline implications and directions for future research.

翻译：面向表格数据的自然语言接口必须处理查询中固有的模糊性。我们并非将模糊性视为缺陷，而是将其重新定义为协作交互的一个特征，即查询规范的责任由用户和系统共同承担。我们建立了一个原则性框架，用以区分协作性查询（即能够产生可解析解释的查询）与无法解析的非协作性查询。将该框架应用于表格问答与分析任务的评估中，我们对15个常用数据集中的查询进行了分析，发现其中混杂了不同类型的查询，这既不足以评估系统的执行准确性，也不足以评估其解释能力。我们的框架及查询分析将视角从消除模糊性转向在解析查询中拥抱协作。这一反思为表格数据自然语言接口的设计与评估提供了更深入的见解，我们据此概述了未来研究的影响与方向。