Compositional Generalization via Neural-Symbolic Stack Machines
It addresses a key limitation in AI for systematic reasoning, though it is incremental in integrating neural and symbolic approaches.
The paper tackles the problem of compositional generalization in deep learning by proposing the Neural-Symbolic Stack Machine (NeSS), which combines neural networks with symbolic execution to achieve 100% generalization performance on benchmarks like SCAN and compositional machine translation.
Despite achieving tremendous success, existing deep learning models have exposed limitations in compositional generalization, the capability to learn compositional rules and apply them to unseen cases in a systematic manner. To tackle this issue, we propose the Neural-Symbolic Stack Machine (NeSS). It contains a neural network to generate traces, which are then executed by a symbolic stack machine enhanced with sequence manipulation operations. NeSS combines the expressive power of neural sequence models with the recursion supported by the symbolic stack machine. Without training supervision on execution traces, NeSS achieves 100% generalization performance in four domains: the SCAN benchmark of language-driven navigation tasks, the task of few-shot learning of compositional instructions, the compositional machine translation benchmark, and context-free grammar parsing tasks.