Previous work has argued that recursive numeral systems optimise the trade-off between lexicon size and average morphosyntatic complexity (Deni\'c and Szymanik, 2024). However, showing that only natural-language-like systems optimise this tradeoff has proven elusive, and the existing solution has relied on ad-hoc constraints to rule out unnatural systems (Yang and Regier, 2025). Here, we argue that this issue arises because the proposed trade-off has neglected regularity, a crucial aspect of complexity central to human grammars in general. Drawing on the Minimum Description Length (MDL) approach, we propose that recursive numeral systems are better viewed as efficient with regard to their regularity and processing complexity. We show that our MDL-based measures of regularity and processing complexity better capture the key differences between attested, natural systems and unattested but possible ones, including "optimal" recursive numeral systems from previous work, and that the ad-hoc constraints from previous literature naturally follow from regularity. Our approach highlights the need to incorporate regularity across sets of forms in studies that attempt to measure and explain optimality in language.
翻译:先前的研究认为,递归数词系统在词汇库大小与平均形态句法复杂度之间实现了最优权衡(Denić与Szymanik,2024)。然而,证明只有类自然语言的系统能优化这一权衡一直难以实现,现有解决方案依赖于临时性约束以排除非自然系统(Yang与Regier,2025)。本文指出,该问题的根源在于所提出的权衡忽略了规律性——这是人类语法中复杂度的一个核心方面。借鉴最小描述长度(MDL)方法,我们提出递归数词系统应被视为在规律性与处理复杂度方面具有高效性。我们证明,基于MDL的规律性与处理复杂度度量能更好地捕捉已证实的自然系统与未证实但可能存在的系统(包括先前研究中的“最优”递归数词系统)之间的关键差异,且先前文献中的临时性约束自然源于规律性。我们的方法强调,在试图测量和解释语言最优性的研究中,必须纳入跨形式集合的规律性考量。