在频率结构化的意义空间中演化语言的组合性
Evolving language compositionality in a frequency-structured meaning space
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中文总结 AI 辅助
该研究通过迭代学习模型发现，频率分布在学习者可整体习得的形式-意义单元上时可塑造语言组合性，若分布在更小单元则会阻碍语言稳定传递。
中文摘要 AI 辅助
迭代学习模型被引入用于研究语言演化，即人类语言的特征属性至少部分是如何通过在语言使用者之间的反复传递而形成的。关键发现是，语言组合性能够自发产生，这是语言通过语言学习瓶颈反复传递的结果。在此，我们探究不同意义的频率发生变化（使得某些意义出现频率远高于其他意义）时，会如何影响其组合性的特征。我们发现，正如在自然语言中观察到的那样，高频意义可以摆脱符合表征低频意义的语法的压力。然而，当频率结构被施加在意义向量的部分而非整体时，尽管最频繁的元素能够被可靠地学习，语言仍无法跨代传递。这些结果表明，仅当频率分布被定义在学习者能够整体习得的形式-意义单元上时，频率才能塑造涌现的语言结构；而当频率被分布在更小的单元上时，它无法支持组合泛化所需的关系结构，从而阻碍稳定的语言传递。
英文摘要
The iterated learning model was introduced to investigate language evolution: the way in which the characteristic properties of human languages have been shaped, at least partly, by repeated transmission from one language user to another. The key finding is that language compositionality can arise spontaneously as a consequence of language being passed repeatedly through a language learning bottleneck. Here we explore how changing the frequency of different meanings, so that some meanings occur much more frequently than others, affects the character of its compositionality. We find that, as observed in natural languages, high-frequency meanings can escape the pressure to conform to the grammar that characterizes lower-frequency meanings. However, when the frequency structure is instead imposed on parts rather than on whole meaning vectors, the language fails to transmit across generations. This occurs despite the fact that the most frequent elements are reliably learned. These results suggest that frequency can shape emergent linguistic structure only when the frequency distribution is defined over form-meaning units that learners can acquire holistically. When frequency is instead distributed over smaller units, it fails to support the relational structure required for compositional generalisation, thereby preventing stable language transmission.
发表机构
- Université de Namur（那慕尔大学）
- Vrije Universiteit Brussel（布鲁塞尔自由大学）
- University of Bristol（布里斯托尔大学）
机构由 AI 辅助整理，请以论文原文为准。
