@InProceedings{enguehard-goldberg-linzen:2017:CoNLL,
  author    = {Enguehard, \'{E}mile  and  Goldberg, Yoav  and  Linzen, Tal},
  title     = {Exploring the Syntactic Abilities of RNNs with Multi-task Learning},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {3--14},
  abstract  = {Recent work has explored the syntactic abilities of RNNs using the subject-verb
	agreement task, which diagnoses sensitivity to sentence structure. RNNs
	performed this task well in common cases, but faltered in complex sentences
	(Linzen et al., 2016). We test whether these errors are due to inherent
	limitations of the architecture or to the relatively indirect supervision
	provided by most agreement dependencies in a corpus. We trained a single RNN to
	perform both the agreement task and an additional task, either CCG supertagging
	or language modeling. Multi-task training led to significantly lower error
	rates, in particular on complex sentences, suggesting that RNNs have the
	ability to evolve more sophisticated syn- tactic representations than shown
	before. We also show that easily available agreement training data can improve
	performance on other syntactic tasks, in particular when only a limited amount
	of training data is available for those tasks. The multi-task paradigm can also
	be leveraged to inject grammatical knowledge into language models.},
  url       = {http://aclweb.org/anthology/K17-1003}
}