We present the first polynomial algorithm for learning a class of inversion transduction grammars (ITGs) that implement context free transducers -- functions from strings to strings. The class of transductions that we can learn properly includes all subsequential transductions. These algorithms are based on a generalisation of distributional learning; we prove correctness of our algorithm under an identification in the limit model.