One hallmark of human language is its combinatoriality -- reusing a
relatively small inventory of building blocks to create a far larger inventory
of increasingly complex structures. In this paper, we explore the idea that
combinatoriality in language reflects a human inductive bias toward
representational efficiency in symbol systems. We develop a computational
framework for discovering structure in a writing system. Built on top of
state-of-the-art library learning and program synthesis techniques, our
computational framework discovers known linguistic structures in the Chinese
writing system and reveals how the system evolves towards simplification under
pressures for representational efficiency. We demonstrate how a library
learning approach, utilizing learned abstractions and compression, may help
reveal the fundamental computational principles that underlie the creation of
combinatorial structures in human cognition, and offer broader insights into
the evolution of efficient communication systems.

人类语言的一个特点是组合性 —— 通过重复使用相对较小的基本单元，创建出较为庞大、越发复杂的结构。本文探索了语言中组合性反映了人类对符号系统中表现效率的归纳偏见这一观点。我们使用先进的库学习和程序合成技术，开发了一个计算框架来发现一种写作系统中的结构。计算框架在中国的写作系统中发现了已知的语言结构，并揭示了系统在表现效率的压力下向简化方向演化的过程。我们演示了如何通过利用学到的抽象和压缩的图书馆学习方法，揭示出构成人类认知中组合结构创造的基本计算原则，并对有效沟通系统的演化提供更广泛的见解。