@InProceedings{gulordava-EtAl:2018:N18-1,
  author    = {Gulordava, Kristina  and  Bojanowski, Piotr  and  Grave, Edouard  and  Linzen, Tal  and  Baroni, Marco},
  title     = {Colorless Green Recurrent Networks Dream Hierarchically},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {1195--1205},
  abstract  = {Recurrent neural networks (RNNs) achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues (``The colorless green ideas I ate with the chair sleep furiously''), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence.},
  url       = {http://www.aclweb.org/anthology/N18-1108}
}