  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.
