However, for LAMBADA, correct answers can range over the entirety of V, making the output embedding function difficult to train.
we also experiment with a modified version of the Stanford Reader that uses the same embedding function e for both input and output words: where W is an additional parameter matrix used to match dimensions and model any additional needed transformation.