2024/05/11 by Guangyuan Jiang, Jiang, Guangyuan, Matthias Hofer +9
Arts and Humanities · Social Sciences · #Computation and Language (cs.CL) #Education and Technology Integration #FOS: Computer and information sciences #Library Science and Information Literacy #Literacy, Media, and Education
paper · pdf · doi:10.48550/arxiv.2405.06906
openalex publication_date 2024/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.