In standard approaches to batch learning, for every training example f (i) , e (i) we enumerate every translation and derivation in the respective sets E ( f (i) ) and D( f (i) ), and attempt to adjust the parameters so we can achieve the translations with the lowest error for the entire data.
as mentioned previously, the entire space of derivations is too large to handle in practice.