This is because for some of the hypotheses, the first word may not always have the highest score.
lSTM ( Figure 2) is a variant of RNN, which computes the hidden state h t using a different approach by adding an internal memory cell c t ∈ R n at every time step t. an lSTM unit considers the input state x t at time step t, the hidden state h t−1 , and the internal memory state c t−1 at time step Figure 1: Encoder-Decoder framework for sequence to sequence learning.