@inproceedings{DoRehbein2018,
  author    = {Bich-Ngoc Do and Ines Rehbein},
  title     = {Evaluating LSTM models for grammatical function labelling},
  series = {Proceedings of the 15th International Conference on Parsing Technologies, September 20–22, 2017 Pisa, Italy (IWPT 2017)},
  publisher = {The Association for Computational Linguistics},
  address   = {Stroudsburg PA, USA},
  isbn      = {978-1-945626-73-9},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-80010},
  pages     = {128 -- 133},
  year      = {2018},
  abstract  = {To improve grammatical function labelling for German, we augment the labelling component of a neural dependency parser with a decision history. We present different ways to encode the history, using different LSTM architectures, and show that our models yield significant improvements, resulting in a LAS for German that is close to the best result from the SPMRL 2014 shared task (without the reranker).},
  language  = {en}
}