x N ] consists of a series of word embeddings each represented as a one-hot vector of size |V |×1, and the output prediction, y, is a vector representing a probability distribution over the vocabulary. At each step, the model uses the word embedding matrix E to retrieve the embedding for the current word, and then combines it with the hidden layer from the previous step to compute a new hidden layer. This hidden layer is then used to generate an output layer which is passed through a softmax layer to generate a probability distribution over the entire vocabulary. That is, at time t: