论文标题
通过神经符号堆栈机的组成概括
Compositional Generalization via Neural-Symbolic Stack Machines
论文作者
论文摘要
尽管取得了巨大的成功，但现有的深度学习模型在构图概括中存在暴露的局限性，学习组成规则并以系统的方式将其应用于看不见的情况。为了解决这个问题，我们提出了神经符号堆栈机（NESS）。它包含一个神经网络来生成轨迹，然后通过序列操作操作增强的符号堆栈计算机执行。 NESS将神经序列模型的表达能力与符号堆栈机支持的递归相结合。在执行轨迹上的培训监督的情况下，NESS在四个领域中实现了100％的概括性能：语言驱动导航任务的扫描基准，几乎没有组成指令学习的任务，组成机器翻译基准和无上下文的语法分析任务。
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.
