论文标题
学习无上下文的语言使用非确定性堆栈RNN
Learning Context-Free Languages with Nondeterministic Stack RNNs
论文作者
论文摘要
我们提出了一个可区分的堆栈数据结构，该数据结构同时且经常根据Lang的算法来模拟非确定性推出自动机。我们将这种数据结构与复发神经网络（RNN）控制器的组合称为非确定堆栈RNN。我们将模型与各种形式语言的现有堆栈RNN进行了比较，这表明我们的模型在确定性任务上更可靠地收敛到算法行为，并在固有的非确定任务上实现了较低的跨透明术。
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.
