@InProceedings{horsmann-zesch:2017:EMNLP2017,
  author    = {Horsmann, Tobias  and  Zesch, Torsten},
  title     = {Do LSTMs really work so well for PoS tagging? -- A replication study},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {727--736},
  abstract  = {A recent study by Plank et al. (2016) found that LSTM-based PoS taggers
	considerably improve over the current state-of-the-art when evaluated on the
	corpora of the Universal Dependencies project that use a coarse-grained tagset.
	We replicate this study using a fresh collection of 27 corpora of 21 languages
	that are annotated with fine-grained tagsets of varying size.
	Our replication confirms the result in general, and we additionally find that
	the advantage of LSTMs is even bigger for larger tagsets.
	However, we also find that for the very large tagsets of morphologically rich
	languages, hand-crafted morphological lexicons are still necessary to reach
	state-of-the-art performance.},
  url       = {https://www.aclweb.org/anthology/D17-1076}
}