A key aspect of human intelligence is the ability to infer abstract rules
directly from high-dimensional sensory data, and to do so given only a limited
amount of training experience. Deep neural network algorithms have proven to be
a powerful tool for learning directly from high-dimensional data, but currently
lack this capacity for data-efficient induction of abstract rules, leading some
to argue that symbol-processing mechanisms will be necessary to account for
this capacity. In this work, we take a step toward bridging this gap by
introducing the Emergent Symbol Binding Network (ESBN), a recurrent network
augmented with an external memory that enables a form of variable-binding and
indirection. This binding mechanism allows symbol-like representations to
emerge through the learning process without the need to explicitly incorporate
symbol-processing machinery, enabling the ESBN to learn rules in a manner that
is abstracted away from the particular entities to which those rules apply.
Across a series of tasks, we show that this architecture displays nearly
perfect generalization of learned rules to novel entities given only a limited
number of training examples, and outperforms a number of other competitive
neural network architectures.

本研究介绍了 Emergent Symbol Binding Network (ESBN)，它是一种采用外部记忆扩展的循环神经网络，能够通过一种变量绑定和间接储存的机制从学习过程中出现符号式表示，使得其能够在抽象规则的学习中达到几乎完美的泛化，摆脱了对显式加工符号的依赖。