Superlatives are used to single out elements with a maximal/minimal property. Semantically, superlatives perform a set comparison: something (or some things) has the min/max property out of a set. As such, superlatives provide an ideal phenomenon for studying implicit phenomena and discourse restrictions. While this comparison set is often not explicitly defined, its (implicit) restrictions can be inferred from the discourse context the expression appears in. In this work we provide an extensive computational study on the semantics of superlatives. We propose a unified account of superlative semantics which allows us to derive a broad-coverage annotation schema. Using this unified schema we annotated a multi-domain dataset of superlatives and their semantic interpretations. We specifically focus on interpreting implicit or ambiguous superlative expressions, by analyzing how the discourse context restricts the set of interpretations. In a set of experiments we then analyze how well models perform at variations of predicting superlative semantics, with and without context. We show that the fine-grained semantics of superlatives in context can be challenging for contemporary models, including GPT-4.

翻译：最高级用于筛选具有最大/最小属性的元素。从语义学角度看，最高级执行集合比较：某物（或某些事物）在特定集合中具有最小/最大属性。因此，最高级为研究隐性现象和语篇限制提供了理想的研究对象。虽然这种比较集合通常未被明确定义，但其（隐性）限制可以从表达式所在的语篇语境中推断出来。本研究对最高级的语义进行了全面的计算分析。我们提出了一个统一的最高级语义解释框架，据此推导出覆盖广泛的标注体系。基于该统一体系，我们标注了跨领域最高级数据集及其语义解释。我们特别关注如何通过分析语篇语境对解释集的限制，来解读隐性或歧义的最高级表达式。通过系列实验，我们分析了模型在不同情境下（含语境与无语境）预测最高级语义变体的表现。研究表明，包括GPT-4在内的当代模型在处理语境中最高级的细粒度语义时仍面临挑战。