@InProceedings{lim-poibeau:2017:K17-3,
  author    = {Lim, KyungTae  and  Poibeau, Thierry},
  title     = {A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations},
  booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {63--70},
  abstract  = {In this paper, we present our multilingual dependency parser developed for the
	CoNLL 2017 UD Shared Task dealing with “Multilingual Parsing from Raw Text to
	Universal Dependencies”. Our parser extends the monolingual BIST-parser as a
	multi-source multilingual trainable parser. Thanks to multilingual word
	embeddings and one hot encodings for languages, our system can use both
	monolingual and multi-source training. We trained 69 monolingual language
	models and 13 multilingual models for the shared task. Our multilingual
	approach making use of different resources yield better results than the
	monolingual approach for 11 languages. Our system ranked 5
	th and achieved 70.93 overall LAS score over the 81 test corpora
	(macro-averaged LAS F1 score).},
  url       = {http://www.aclweb.org/anthology/K17-3006}
}