  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.
