For its candidate generation phase, MERT generates the k-best candidate translations for each source sentence according to h w , where w is the weight vector from the previous optimization phase (or an arbitrary weight vector for the first iteration).
for its optimization phase, MERT defines the loss function as follows: it prefers weight vectors w such that the gold function G scores H w 's best policy as highly as possible (if H w 's best policy is the same as G's best policy, then there is zero loss).