We present a differentiable stack data structure that simultaneously and tractably encodes an exponential number of stack configurations, based on Lang's algorithm for simulating nondeterministic pushdown automata. We call the combination of this data structure with a recurrent neural network (RNN) controller a Nondeterministic Stack RNN. We compare our model against existing stack RNNs on various formal languages, demonstrating that our model converges more reliably to algorithmic behavior on deterministic tasks, and achieves lower cross-entropy on inherently nondeterministic tasks.

翻译：我们提出了一个不同的堆叠数据结构,根据兰的模拟非决定性的自下而下自上而下自上而下自动的算法,同时和可移动地编码成成指数数的堆叠配置。我们把这一数据结构与一个经常性神经网络控制器(RNN)的组合称为非决定性的斯塔克-RNN。我们比较了我们的模式与各种正式语言的现有堆叠RNN的模型,表明我们的模型更可靠地结合到确定性任务的算法行为上,并在固有的非决定性任务上实现了较低的交叉性。