arXiv cs.CL· Sally Dong, Luke Thorburn, Andrew Konya, Michiel Bakker·· 14 小时前AI 评分27
一种基于情境的规范性文本歧义度量方法
A Contextual Measure of Ambiguity in Prescriptive Text
AI 导读
研究者提出将规范性文本的歧义量化为多方“评分者”对备选政策在各相关“案例”中适用方式的分歧程度，而非孤立地衡量文本被如何感知。在两个政策领域的初步分析显示，即便使用模拟评分者，该度量也与真实人类分歧相关，且对虚假文本特征具有鲁棒性。作者随后将该度量应用于现有审议数据集，描述政策歧义与政策支持度之间的关联。
正文 · 原文
Abstract:Ambiguity in prescriptive text such as laws, contracts, rules, policies, or recommendations can be both desirable (e.g., if it enables a law to adapt to changing context, such as new technologies) and undesirable (e.g., if it leads to arbitrary discretion or conflict over the law's interpretation). To help navigate these trade-offs, it would be valuable to have methods for quantifying ambiguity that are predictive of such outcomes, but existing measures capture how text is perceived in isolation rather than how it is interpreted in context. In this paper, we propose quantifying ambiguity as the degree of disagreement among a set of stakeholders ("raters") on how alternative prescriptive texts ("policies") apply in each of a set of relevant scenarios ("cases"). Using two policy domains, we present preliminary analysis suggesting that, even when using simulated raters, this approach (i) correlates with real human disagreement, and (ii) is robust to spurious text features. We then apply the measure to existing deliberation datasets to describe the association between policy ambiguity and support for those policies.
| Comments: | 10 pages, EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | K.4.1 |
| Cite as: | arXiv:2610.35461 [cs.CL] |
| (or arXiv:2610.35461v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35461 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sally Dong [view email]
[v1]
Mon, 26 Oct 2026 13:56:28 UTC (1,168 KB)
来源：arXiv cs.CL · arxiv.org