@inproceedings{wilcox-etal-2018-rnn,
    title = "What do {RNN} Language Models Learn about Filler{--}Gap Dependencies?",
    author = "Wilcox, Ethan  and
      Levy, Roger  and
      Morita, Takashi  and
      Futrell, Richard",
    editor = "Linzen, Tal  and
      Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-5423/",
    doi = "10.18653/v1/W18-5423",
    pages = "211--221",
    abstract = "RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they learn. Here we investigate whether state-of-the-art RNN language models represent long-distance \textbf{filler{--}gap dependencies} and constraints on them. Examining RNN behavior on experimentally controlled sentences designed to expose filler{--}gap dependencies, we show that RNNs can represent the relationship in multiple syntactic positions and over large spans of text. Furthermore, we show that RNNs learn a subset of the known restrictions on filler{--}gap dependencies, known as \textbf{island constraints}: RNNs show evidence for wh-islands, adjunct islands, and complex NP islands. These studies demonstrates that state-of-the-art RNN models are able to learn and generalize about empty syntactic positions."
}