  We demonstrate that a dependency parser can be built using a credit
assignment compiler which removes the burden of worrying about low-level
machine learning details from the parser implementation. The result is a simple
parser which robustly applies to many languages that provides similar
statistical and computational performance with best-to-date transition-based
parsing approaches, while avoiding various downsides including randomization,
extra feature requirements, and custom learning algorithms.
