@InProceedings{yu-vu:2017:Short,
  author    = {Yu, Xiang  and  Vu, Ngoc Thang},
  title     = {Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {672--678},
  abstract  = {We present a transition-based dependency parser that uses a convolutional
	neural network to compose word representations from characters. The character
	composition model shows great improvement over the word-lookup model,
	especially
	for parsing agglutinative languages. These improvements are even better than
	using pre-trained word
	embeddings from extra data. On the SPMRL data sets, our system outperforms the
	previous best greedy parser (Ballesteros et. al, 2015) by a margin of 3% on
	average.},
  url       = {http://aclweb.org/anthology/P17-2106}
}