@InProceedings{heigold-neumann-vangenabith:2017:EACLlong,
  author    = {Heigold, Georg  and  Neumann, Guenter  and  van Genabith, Josef},
  title     = {An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {505--513},
  abstract  = {This paper investigates neural character-based morphological tagging 
	for languages with complex morphology and large tag sets.
	Character-based approaches are attractive as they can handle rarely- and unseen
	words gracefully.
	We evaluate on 14 languages and 
	observe consistent gains over a state-of-the-art morphological tagger 
	across all languages except for English and French, where we match the
	state-of-the-art.
	We compare two architectures for computing character-based word vectors using
	recurrent (RNN) and convolutional (CNN) nets. 
	We show that the CNN based approach performs slightly worse and less
	consistently than the RNN based approach.
	Small but systematic gains are observed when combining the two architectures by
	ensembling.},
  url       = {http://www.aclweb.org/anthology/E17-1048}
}