Searn (Daumé III et al., 2009) and DAgger (Ross et al., 2011) are two popular principled frameworks for reducing sequential prediction to classification by learning a classifier on additional synthetic training data.
as we do in our experiments, practitioners often see good results by training on the gold standard labels with an off-the-shelf classification algorithm, as though classifying IID data (Bengtson and Roth, 2008;Choi and Palmer, 2012).