We introduce two simple improvements to the lexical weighting features of Koehn, Och, and Marcu for machine translation: one which smooths the probability of translating word f to word e by simplifying English morphology, and one which conditions it on the kind of training data that f and e co-occurred in. These new variations lead to improvements of up to +0.8 BLEU, with an average improvement of +0.6 BLEU across two language pairs, two genres, and two translation systems.