Reordering can be done either using rules based on linguistic knowledge (Isozaki et al., 2010b;Collins et al., 2005) or learning from aligned parallel data (Xia and McCord, 2004;Habash, 2007), and in principle our pseudo-corpus creation paradigm is compatible with any of these methods.
in this work we utilize rule-based methods, as our goal is to improve translation of low-resource languages, where large quantities of high-quality parallel data do not exist and we posit that current data-driven reordering methods are unlikely to function well.