However, more accurate feature learning using continuous representations and nonlinear models has allowed parsing architectures to be simplified.
most recent transition-based parsers have moved back to local learning and greedy inference, seemingly without losing accurracy (Chen and Manning, 2014;Kiperwasser and Goldberg, 2016).