@InProceedings{coavoux-crabbe:2017:EACLshort,
  author    = {Coavoux, Maximin  and  Crabb\'{e}, Benoit},
  title     = {Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {331--336},
  abstract  = {We introduce a constituency parser based
	on a bi-LSTM encoder adapted from re-
	cent work (Cross and Huang, 2016b;
	Kiperwasser and Goldberg, 2016), which
	can incorporate a lower level character bi-
	LSTM (Ballesteros et al., 2015; Plank et
	al., 2016). We model two important in-
	terfaces of constituency parsing with aux-
	iliary tasks supervised at the word level:
	(i) part-of-speech (POS) and morpholog-
	ical tagging, (ii) functional label predic-
	tion. On the SPMRL dataset, our parser
	obtains above state-of-the-art results on
	constituency parsing without requiring ei-
	ther predicted POS or morphological tags,
	and outputs labelled dependency trees.},
  url       = {http://www.aclweb.org/anthology/E17-2053}
}