@InProceedings{ptz-glocker:2019:S19-2,
  author    = {Pütz, Tobias  and  Glocker, Kevin},
  title     = {Tüpa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {113--118},
  abstract  = {Our submission for Task~1 `Cross-lingual Semantic Parsing with UCCA' at SemEval-2018 is a feed-forward neural network that builds upon an existing state-of-the-art transition-based directed acyclic graph parser. We replace most of its features by deep contextualized word embeddings and introduce an approximation to represent non-terminal nodes in the graph as an aggregation of their terminal children. We further demonstrate how augmenting data using the baseline systems provides a consistent advantage in all open submission tracks. We submitted results to all open tracks (English, in- and out-of-domain, German in-domain and French in-domain, low-resource). Our system achieves competitive performance in all settings besides the French, where we did not augment the data. Post-evaluation experiments showed that data augmentation is especially crucial in this setting.},
  url       = {http://www.aclweb.org/anthology/S19-2016}
}