Structural Inductive Biases in Emergent Communication

• URL: http://arxiv.org/abs/2002.01335v4
• Date: Tue, 27 Jul 2021 04:13:03 GMT
• Title: Structural Inductive Biases in Emergent Communication
• Authors: Agnieszka S{\l}owik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, Sean B. Holden, Christopher Pal
• Abstract summary: We investigate the impact of representation learning in artificial agents by developing graph referential games. We show that agents parametrized by graph neural networks develop a more compositional language compared to bag-of-words and sequence models.
• Score: 36.26083882473554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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