@inproceedings{yu-vu-2017-character,
    title = "Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages",
    author = "Yu, Xiang  and
      Vu, Ngoc Thang",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P17-2106/",
    doi = "10.18653/v1/P17-2106",
    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."
}