Set decoder D: Due to its powerful ability of LSTM to model sequence dependency, we also implement D as a LSTM model to capture highorder correlations between labels.
the hidden state s t of the set decoder D at timestep t is computed as: where [e(y t−1 ); c t ] denotes the concatenation of vectors e(y t−1 ) and c t , e(y t−1 ) is the embedding of the label y t−1 generated at the last time-step, and c t is the context vector obtained by the attention mechanism.