Context Shapes Emergent Communication about Concepts at Different Levels of Abstraction

We study the communication of concepts at different levels of abstraction and in different contexts in an agent-based, interactive reference game. While playing a concept-level reference game, the neural network agents develop a communication system from scratch. We use a novel symbolic dataset that disentangles concept type (ranging from specific to generic) and context (ranging from fine to coarse) to study the influence of these factors on the emerging language. We compare two game scenarios: one in which speaker agents have access to context information (context-aware) and one in which the speaker agents do not have access to context information (context-unaware). First, we find that the agents learn higher-level concepts from the object inputs alone. Second, an analysis of the emergent communication system shows that only context-aware agents learn to communicate efficiently by adapting their messages to the context conditions and relying on context for unambiguous reference. Crucially, this behavior is not explicitly incentivized by the game, but efficient communication emerges and is driven by the availability of context alone. The emerging language we observe is reminiscent of evolutionary pressures on human languages and highlights the pivotal role of context in a communication system.

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Recommended citation: Kobrock, Kristina, Ohmer, Xenia, Bruni, Elia & Gotzner, Nicole (2024). “Context Shapes Emergent Communication about Concepts at Different Levels of Abstraction.” Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).