@inproceedings{dusell-chiang-2020-learning,
    title = "Learning Context-free Languages with Nondeterministic Stack {RNN}s",
    author = "DuSell, Brian  and
      Chiang, David",
    editor = "Fern{\'a}ndez, Raquel  and
      Linzen, Tal",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.conll-1.41/",
    doi = "10.18653/v1/2020.conll-1.41",
    pages = "507--519",
    abstract = "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."
}