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Related Experiment Video

Updated: Jun 4, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Deep neural networks and humans both benefit from compositional language structure.

Lukas Galke^1,2, Yoav Ram^3,4, Limor Raviv^5,6

¹Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark. galke@imada.sdu.dk.

Nature Communications

|December 31, 2024

Summary

Deep neural networks (DNNs) learn structured languages better, similar to humans. More compositional languages lead to systematic generalization in DNNs, improving their use as models for human language acquisition.

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Area of Science:

Computational Linguistics
Cognitive Science
Artificial Intelligence

Background:

Deep neural networks (DNNs) excel in natural language processing.
Human language learning favors compositional and transparent structures.
DNNs' advantage with structured linguistic input remains underexplored.

Purpose of the Study:

To compare human and DNN learning of languages with varying compositional structures.
To investigate if DNNs exhibit a learnability advantage for structured languages.
To assess DNNs' potential as models for human language acquisition.

Main Methods:

Evaluating large language models and recurrent neural networks.
Testing memorization and generalization capabilities.
Comparing DNN performance across languages with different compositional degrees.

Main Results:

DNNs show a learnability advantage for more compositional languages.
Structured linguistic input leads to more systematic generalization in DNNs.
DNNs' learning patterns align more closely with human learners for compositional languages.

Conclusions:

DNNs, like humans, benefit from linguistic structure.
Compositionality enhances systematic generalization in deep learning models.
Findings support the use of DNNs as models for human language learning.