2 In this paper, we propose a new framework of the binarized NNJM (BNNJM), which is similar to the NNJM but learns not to predict the next word given the context, but solves a binary classification problem by adding a variable v ∈ {0, 1} that stands for whether the current target word t i is correctly/wrongly produced in terms of source context words s The BNNJM is learned by a feedforward neural network with m + n inputs s Because the BNNJM uses the current target word as input, the information about the current target word can be combined with the context word information and processed in the hidden layers.
the hidden layers can be used to learn the difference between correct target words and noise in the BNNJM, while in the NNJM the hidden layers just contain information about context words and only the output layer can be used to discriminate between the training data and noise, giving the BNNJM more power to learn this classification problem.