The Surprising Computational Power of Nondeterministic Stack RNNs

TL;DR

This paper shows that nondeterministic stack RNNs can recognize a broader class of languages, including some non-context-free ones, and introduces a vector-based stack model that improves language modeling performance.

Contribution

The paper reveals unexpected capabilities of nondeterministic stack RNNs and proposes a vector-based stack model to enhance their information capacity.

Findings

Recognize non-context-free languages

Handle larger alphabet sizes effectively

Improve perplexity on Penn Treebank benchmark

Abstract

Traditional recurrent neural networks (RNNs) have a fixed, finite number of memory cells. In theory (assuming bounded range and precision), this limits their formal language recognition power to regular languages, and in practice, RNNs have been shown to be unable to learn many context-free languages (CFLs). In order to expand the class of languages RNNs recognize, prior work has augmented RNNs with a nondeterministic stack data structure, putting them on par with pushdown automata and increasing their language recognition power to CFLs. Nondeterminism is needed for recognizing all CFLs (not just deterministic CFLs), but in this paper, we show that nondeterminism and the neural controller interact to produce two more unexpected abilities. First, the nondeterministic stack RNN can recognize not only CFLs, but also many non-context-free languages. Second, it can recognize languages with much larger alphabet sizes than one might expect given the size of its stack alphabet. Finally, to increase the information capacity in the stack and allow it to solve more complicated tasks with large alphabet sizes, we propose a new version of the nondeterministic stack that simulates stacks of vectors rather than discrete symbols. We demonstrate perplexity improvements with this new model on the Penn Treebank language modeling benchmark.