Compositional generalization is a basic and essential intellective capability
of human beings, which allows us to recombine known parts readily. However,
existing neural network based models have been proven to be extremely deficient
in such a capability. Inspired by work in cognition which argues
compositionality can be captured by variable slots with symbolic functions, we
present a refreshing view that connects a memory-augmented neural model with
analytical expressions, to achieve compositional generalization. Our model
consists of two cooperative neural modules, Composer and Solver, fitting well
with the cognitive argument while being able to be trained in an end-to-end
manner via a hierarchical reinforcement learning algorithm. Experiments on the
well-known benchmark SCAN demonstrate that our model seizes a great ability of
compositional generalization, solving all challenges addressed by previous
works with 100% accuracies.

通过基于神经网络和具有符号功能的可变插槽进行变通泛化的记忆增强神经模型，该模型由两个协同神经模块组成，一个是 Composer，另一个是 Solver，通过分层强化学习算法进行端到端训练，实现变通组合能力，其在 well-known benchmark SCAN 上的实验表明，我们的模型具有极强的组合泛化能力，以 100％精度解决了之前工作中面临的所有挑战。