@inproceedings{yu-etal-2018-approximate,
    title = "Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning",
    author = "Yu, Xiang  and
      Vu, Ngoc Thang  and
      Kuhn, Jonas",
    editor = "de Marneffe, Marie-Catherine  and
      Lynn, Teresa  and
      Schuster, Sebastian",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W18-6021/",
    doi = "10.18653/v1/W18-6021",
    pages = "183--191",
    abstract = "We present a general approach with reinforcement learning (RL) to approximate dynamic oracles for transition systems where exact dynamic oracles are difficult to derive. We treat oracle parsing as a reinforcement learning problem, design the reward function inspired by the classical dynamic oracle, and use Deep Q-Learning (DQN) techniques to train the oracle with gold trees as features. The combination of a priori knowledge and data-driven methods enables an efficient dynamic oracle, which improves the parser performance over static oracles in several transition systems."
}