This method works significantly better than standard perceptron, and is followed by later incremental parsers, for instance in (Zhang and Clark, 2008;Huang and Sagae, 2010).
two problems remain: first, up till now there has been no theoretical justification for early update; and secondly, it makes learning extremely slow as witnessed by the above-cited papers because it only learns on partial examples and often requires 15-40 iterations to converge while normal perceptron converges in 5-10 iterations (Collins, 2002).