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BibTeX

@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",
    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://www.aclweb.org/anthology/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.",
}