@InProceedings{vilares-gmezrodrguez:2018:UDW2018,
  author    = {Vilares, David  and  Gómez-Rodríguez, Carlos},
  title     = {Transition-based Parsing with Lighter Feed-Forward Networks},
  booktitle = {Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {162--172},
  abstract  = {We explore whether it is possible to build lighter parsers, that are statistically equivalent to their corresponding standard version, for a wide set of languages showing different structures and morphologies. As testbed, we use the Universal Dependencies and transition-based dependency parsers trained on feed-forward networks. For these, most existing research assumes de facto standard embedded features and relies on pre-computation tricks to obtain speedups. We explore how these features and their size can be reduced and whether this translates into speed-ups with a negligible impact on accuracy. The experiments show that grand-daughter features can be removed for the majority of treebanks without a significant (negative or positive) LAS difference. They also show how the size of the embeddings can be notably reduced.},
  url       = {http://www.aclweb.org/anthology/W18-6019}
}