Prior research has demonstrated that joint prediction alleviates error propagation inherent in pipeline architectures, where mistakes cascade from one task to the next (Bohnet et al., 2013;Tratz, 2013;Hatori et al., 2012;Zhang et al., 2014a).
jointly modeling all the processing tasks inevitably increases inference complexity.