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Tensors and compositionality in neural systems.

Andrea E Martin¹, Leonidas A A Doumas²

¹Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences

|December 17, 2019

Summary

This summary is machine-generated.

Tensor product (TP) systems fail to model how humans compose meaning, violating variable-value independence. A new binding mechanism is proposed for neural systems to achieve human-like compositionality in thought and language.

Keywords:

binding compositionality concepts language predicates tensor products

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Area of Science:

Cognitive Science
Neuroscience
Artificial Intelligence
Computational Linguistics

Background:

Current models of meaning composition lack a specified binding operator.
Tensor product (TP) based systems are widely used but may not accurately reflect human cognitive processes.

Purpose of the Study:

To challenge the efficacy of tensor product (TP) based binding in neurophysiological computation for meaning composition.
To propose an alternative binding mechanism that supports compositionality in neural systems.

Main Methods:

Simulations of TP-based systems.
Two behavioral experiments investigating human judgments of similarity in compositional structures.

Main Results:

TP systems violate variable-value independence, unlike human behavior.
Humans perceive similarity based on shared roles (e.g., 'fuzzy' applied to different objects), a property not captured by TPs when objects are dissimilar in isolation.

Conclusions:

TP binding mechanisms are inadequate for approximating human compositional representation.
A novel binding mechanism is needed to ensure role-argument independence for neural systems to achieve human-like compositionality and expressive power.