@InProceedings{yu-vu-kuhn:2018:UDW2018,
  author    = {Yu, Xiang  and  Vu, Ngoc Thang  and  Kuhn, Jonas},
  title     = {Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning},
  booktitle = {Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-6021}
}