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.

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，而是将其重构为协作交互的一种特征——用户会依据其意图来设定查询的明确程度。我们基于用户与系统之间查询规范化的共同责任，建立了一个原则性框架，区分了明确查询与模糊协作查询（系统可通过合理推断予以解决）以及无法解决的非协作查询。通过将该框架应用于表格问答与分析任务的评估，我们对15个数据集中的查询进行了分析，发现当前评估方法存在查询类型的无控混杂，既不足以准确评估系统性能，也无法有效检验其解释能力。这种围绕查询解决过程中协作关系的概念化思考，为设计并评估面向表格数据分析的自然语言接口提供了理论依据，我们据此提炼出未来研究的具体方向与更广泛的应用启示。