Synthesizing theories of human language with Bayesian program induction

Kevin Ellis; Adam Albright; Armando Solar-Lezama; Joshua B. Tenenbaum; Timothy J. O’Donnell

doi:10.1038/s41467-022-32012-w

Nature Communications (Aug 2022)

Synthesizing theories of human language with Bayesian program induction

Kevin Ellis,
Adam Albright,
Armando Solar-Lezama,
Joshua B. Tenenbaum,
Timothy J. O’Donnell

Affiliations

Kevin Ellis: Department of Computer Science, Cornell University
Adam Albright: Department of Linguistics, Massachusetts Institute of Technology
Armando Solar-Lezama: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
Joshua B. Tenenbaum: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
Timothy J. O’Donnell: Department of Linguistics, McGill University

DOI: https://doi.org/10.1038/s41467-022-32012-w
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 13

Abstract

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Humans can infer rules for building words in a new language from a handful of examples, and linguists also can infer language patterns across related languages. Here, the authors provide an algorithm which models these grammatical abilities by synthesizing human-understandable programs for building words.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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