We give a new theoretical motivation for stacking parsers, in terms of extending a parsing model's feature space.
we view stacked learning as a way of approximating non-local features in a linear model, rather than making empirically dubious independence (McDonald et al., 2005b) or structural assumptions (e.g., projectivity, Eisner, 1996), using search approximations (Sagae and Lavie, 2005;, solving a (generally NP-hard) integer linear program (Riedel and Clarke, 2006), or adding latent variables (Titov and Henderson, 2007).