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