On the Optimality of Kinship Naming: an Information-theoretic Approach
cs.CL, cs.AI
Submitted: 2025-11-24
Updated: 2026-08-31
Comments: EMNLP 2026 (accepted)
Code: https://github.com/meeslindeman/kinship
License: http://creativecommons.org/licenses/by/4.0/
The gist: The structure of naming systems in natural languages hinges on a trade-off between high informativeness and low complexity.
Terminology
Abstract
The structure of naming systems in natural languages hinges on a trade-off between high informativeness and low complexity. Focusing on the domain of kinship naming, we analyze such trade-off while addressing simplifying assumptions of prior work, namely: (i) universal communicative need across languages, and (ii) optimal listeners. To that aim, we collect data from four different languages, and analyze how different communicative needs and variations in the listener model influence the informativeness--complexity trade-off. Adopting a referential game setup from emergent communication, we further show that trade-off optimality is not only theoretically achievable but also emerges empirically in learned communication systems.
Sources
- Fast Graph Representation Learning with PyTorch Geometric
- Emergent Multi-Agent Communication in the Deep Learning Era
- The information bottleneck method
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