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.
@article{arxiv.1705.10814,
title = {Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages},
author = {Xiang Yu and Ngoc Thang Vu},
journal= {arXiv preprint arXiv:1705.10814},
year = {2017}
}