The model is trained to give two kinds of outputs to the word class layer (v t ) as well as to the output word layer (w t+1 ) (Mikolov et al., 2011).
it produces the posterior probability P (v t |s t ) of the word class v t given the current state s t , and the posterior probability P (w t+1 |v t , s t ) of the next word w t+1 given the current word class and LSTM state.