On the Optimality of Kinship Naming: an Information-theoretic Approach
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Abstract (arXiv)
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.
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References · 7
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
- 2016Multi-Agent Cooperation and the Emergence of (Natural) Languagecited by 185
- 2020Emergent Multi-Agent Communication in the Deep Learning Eracited by 170
- 2017Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbolscited by 125
- 2019EGG: a toolkit for research on Emergence of lanGuage in Gamescited by 42
- 2019Semantic categories of artifacts and animals reflect efficient codingcited by 13
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