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arXiv · 2002.01335

Structural Inductive Biases in Emergent Communication

Agnieszka Słowik·Abhinav Gupta·William L. Hamilton·Mateja Jamnik·Sean B. Holden·Christopher Pal
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Abstract

In order to communicate, humans flatten a complex representation of ideas and their attributes into a single word or a sentence. We investigate the impact of representation learning in artificial agents by developing graph referential games. We empirically show that agents parametrized by graph neural networks develop a more compositional language compared to bag-of-words and sequence models, which allows them to systematically generalize to new combinations of familiar features.

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Agnieszka Słowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, Sean B. Holden, Christopher Pal. 2020-02-04. Structural Inductive Biases in Emergent Communication. https://arxiv.org/abs/2002.01335

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