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State-Regularized Recurrent Neural Networks

Cheng Wang, Mathias Niepert

Proc. of the 36th International Conference on Machine Learning (ICML), pp. 6596–6606, 2019.


Recurrent neural networks are a widely used class of neural architectures with two shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We evaluate state-regularized RNNs on (1) regular languages for the purpose of automata extraction; (2) nonregular languages such as balanced parentheses, palindromes, and the copy task where external memory is required; and (3) real-word sequence learning tasks for sentiment analysis, visual object recognition, and language modeling. We show that state-regularization simplifies the extraction of finite state automata from the RNN’s state transition dynamics; forces RNNs to operate more like automata with external memory and less like finite state machines; and makes RNNs more interpretable.



@inproceedings{wang19_icml, title = {State-Regularized Recurrent Neural Networks}, author = {Wang, Cheng and Niepert, Mathias}, year = {2019}, booktitle = {Proc. of the 36th International Conference on Machine Learning (ICML)}, preprint = {https://arxiv.org/abs/1901.08817}, pages = {6596--6606}, url = {https://proceedings.mlr.press/v97/wang19j.html} }