Given that the initial word representations in a span n i are x 1 , ..., x T , we denote their representations x * 1 , ..., x * T after encoded by the biLSTM.
then we obtain the inner-span attention by where α t is computed via a standard feed-forward neural network we have the weighted embedding of each spanx i througĥ Afterwards, we concatenate the starting (x * start ) and ending (x * end ) embedding of each span, as well as its weighted embedding (x i ) and the length feature (φ(i)) to form its final representation e: Once the span representation of n ∈ N and p are obtained, we compute F c for each n with a standard feed-forward neural network: where is the element-wise multiplication.