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arXiv · 1809.02836

Context-Free Transductions with Neural Stacks

Yiding Hao·William Merrill·Dana Angluin·Robert Frank·Noah Amsel·Andrew Benz·Simon Mendelsohn
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Abstract

This paper analyzes the behavior of stack-augmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, context-free language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.

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Yiding Hao, William Merrill, Dana Angluin, Robert Frank, Noah Amsel, Andrew Benz, Simon Mendelsohn. 2018-09-08. Context-Free Transductions with Neural Stacks. https://arxiv.org/abs/1809.02836

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