重新定义表格查询中的模糊性，提出合作式问答框架
Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis
- 将用户查询的模糊性视为合作交互特征而非缺陷
- 在15个数据集上发现查询类型混杂，影响评估有效性
- 为表格自然语言接口的设计与评测提供新方向
自然语言接口在处理表格数据时必须应对查询固有的模糊性。我们不再将模糊性视为缺陷，而是将其重构为用户在查询指定程度上的有意行为，体现协作互动特征。为此，我们提出一个基于用户与系统共同承担查询规范责任的原理性框架，区分可合理推断解决的模糊协作查询与无法解决的非协作查询。将该框架应用于表格问答与分析的评估，我们在15个数据集上分析了查询，发现查询类型未经控制地混杂，既无法准确评估系统性能，也无法有效评估解释能力。这一关于协作性的概念化为表格数据自然语言接口的设计与评估提供了指导，并提炼出未来研究的具体方向及更广泛影响。
原文摘要 · Abstract (English)
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.
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