Experiment settings.

In language emergence, neural agents acquire communication skills by interacting with one another and the environment. Through these interactions, agents learn to connect or ground their observations to the messages they utter, forming a shared consensus about the meaning of the messages. Such connections form what we refer to as a grounding map. However, these maps can often be complicated, unstructured, and contain redundant connections. In this paper, we introduce two novel functional pressures, modeled as differentiable auxiliary losses, to simplify and structure the grounding maps. The first pressure enforces compositionality via topological similarity, which has been previously discussed but has not been modeled or utilized as a differentiable auxiliary loss. The second functional pressure, which is conceptually novel, imposes sparsity in the grounding map by pruning weaker connections while strengthening the stronger ones. We conduct experiments in multiple value-attribute environments with varying communication channels. Our methods achieve improved out-of-domain regularization and rapid convergence over baseline approaches. Furthermore, introduced functional pressures are robust to the changes in experimental conditions and able to operate with minimum training data. We note that functional pressures cause simpler and more structured emergent languages showing distinct characteristics depending on the functional pressure employed. Enhancing grounding map sparsity yields the best performance and the languages with the most compressible grammar. In summary, our novel functional pressures, focusing on compositionality and sparse groundings, expedite the development of simpler, more structured languages while enhancing their generalization capabilities. Exploring alternative types of functional pressures and combining them in agent training may be beneficial in the ongoing quest for improved emergent languages.在语言涌现（language emergence）研究领域中，神经智能体（neural agent）通过彼此交互以及与环境的互动来习得沟通技能。借助此类交互过程，智能体能够将自身的观测结果与发出的信息进行关联，或是完成符号接地（grounding），进而就信息的语义内涵达成共享共识。此类关联便构成了我们所称的符号接地映射（grounding map）。然而这类映射往往结构复杂、缺乏规整性，且存在冗余关联。 本文提出两种全新的功能性压力（functional pressure），将其建模为可微辅助损失函数（differentiable auxiliary loss），用于简化并规整符号接地映射。第一种压力通过拓扑相似性强化组合性（compositionality）——该概念此前已有相关讨论，但尚未被建模为可微辅助损失函数并加以实际利用。第二种功能性压力在概念上具有创新性，它通过剪去弱关联、强化强关联的方式，为符号接地映射引入稀疏性约束。 我们在多种具备不同沟通通道的价值-属性（value-attribute）环境中开展了实验。相较于基线方法，我们的方法在域外正则化（out-of-domain regularization）效果与快速收敛性上均取得了更优表现。此外，所提出的功能性压力对实验条件的变化具备鲁棒性，且仅需极少量训练数据即可生效。 我们注意到，不同的功能性压力会催生出特性各异的更简洁、更具规整性的涌现语言（emergent language）。其中强化符号接地映射的稀疏性可获得最优性能，且催生的语言拥有最易压缩的语法结构。 综上，我们提出的聚焦于组合性与稀疏接地的全新功能性压力，能够加速更简洁、更具规整性的语言的生成，同时提升其泛化能力。在当前持续探索优化涌现语言的研究进程中，探索其他类型的功能性压力并将其结合应用于智能体训练，或将带来可观的研究收益。