We follow<cite> Li et al. (2015)</cite> , taking POS-tagging for case study, using the methods of Jiang et al. (2009) and<cite> Li et al. (2015)</cite> as the discrete stacking and multi-view training baselines, respectively, and building neural network counterparts to their models for empirical comparison. First, it is free from the necessity of manual cross-labelset interactive feature engineering, which is far from trivial for representing annotation correspondence <cite>(Li et al., 2015)</cite> .