Our goal is to develop a classifier of such tuples that decides whether t 1 is a better translation than t 2 given the reference r. Engineering features for deciding whether t 1 is a better translation than t 2 is a difficult task.
we rely on the automatic feature extraction enabled by the SKL framework, and our task is reduced to choosing: (i) a meaningful structural representation for t 1 , t 2 , r , and (ii) a feature function φ mt that maps such structures to substructures, i.e., our feature space.