The iterated learning model is an agent-based model of language change in which language is transmitted from a tutor to a pupil which itself becomes a tutor to a new pupil, and so on. Languages that are stable, expressive, and compositional arise spontaneously as a consequence of a language transmission bottleneck. Previous models have implemented an agent's mapping from signals to meanings using an artificial neural network decoder, but have relied on an unrealistic and computationally expensive process of obversion to implement the associated encoder, mapping from meanings to signals. Here, a new model is presented in which both decoder and encoder are neural networks, trained separately through supervised learning, and trained together through unsupervised learning in the form of an autoencoder. This avoids the substantial computational burden entailed in obversion and introduces a mixture of supervised and unsupervised learning as observed during human development.

翻译：迭代学习模型是一种基于智能体的语言演化模型，其中语言由导师传递给学习者，而学习者随后又成为新一代学习者的导师，如此循环往复。稳定、表达力强且具有组合性的语言会因语言传递瓶颈而自发涌现。先前模型采用人工神经网络解码器实现智能体从信号到意义的映射，但依赖一种不切实际且计算成本高昂的逆向推导过程来实现对应的编码器（从意义到信号的映射）。本文提出一种新模型，其中解码器与编码器均为神经网络，分别通过监督学习进行训练，并通过自编码器形式的无监督学习进行联合训练。这避免了逆向推导带来的巨大计算负担，并引入了人类发展过程中观察到的监督学习与无监督学习的混合机制。