Accelerating language emergence by functional pressures

Area of Science:

• Artificial Intelligence
• Computational Linguistics
• Cognitive Science

Background:

• Neural agents develop communication through interaction, forming grounding maps.
• Existing grounding maps can be complex, unstructured, and redundant.
• Simplifying and structuring these maps is crucial for efficient language emergence.

Purpose of the Study:

• Introduce novel functional pressures to simplify and structure grounding maps.
• Improve the compositionality and sparsity of emergent languages.
• Enhance the generalization capabilities of communication systems.

Main Methods:

• Modeled two differentiable auxiliary losses as functional pressures.
• First pressure enforces compositionality via topological similarity.
• Second pressure imposes sparsity by pruning weak connections.

Main Results:

• Achieved improved out-of-domain regularization and rapid convergence.
• Demonstrated robustness to experimental variations and minimal training data requirements.
• Functional pressures resulted in simpler, more structured emergent languages.

Conclusions:

• Novel functional pressures expedite the development of simpler, more structured emergent languages.
• Grounding map sparsity yields the best performance and most compressible grammars.
• Further exploration of functional pressures can enhance emergent language capabilities.