  Error propagation is a common problem in NLP. Reinforcement learning explores
erroneous states during training and can therefore be more robust when mistakes
are made early in a process. In this paper, we apply reinforcement learning to
greedy dependency parsing which is known to suffer from error propagation.
Reinforcement learning improves accuracy of both labeled and unlabeled
dependencies of the Stanford Neural Dependency Parser, a high performance
greedy parser, while maintaining its efficiency. We investigate the portion of
errors which are the result of error propagation and confirm that reinforcement
learning reduces the occurrence of error propagation.
