The classic tuning architecture used in the dominant MERT approach (Och, 2003) forms the translation subset and learns weight vector w via Source Sentence candidate generation: choose index pairs (i, j); for each, add j to J (i) optimization: find vector w that minimizes l s (H w , G) 5: return w  Figure 2 shows the pseudocode.
we repeated the scalability study from Section 3, now using our pairwise ranking optimization (hereafter, PRO) approach.