Let initial word embeddings in a span s i be denoted as x 1 , ..., x T and their encoded representation be x * 1 , ..., x * T .
the weighted embeddings of each spanx i is obtained bŷ where a t is the inner-span attention computed by where α t is a standard feed-forward neural net- Finally, the starting (x * start ) and ending (x * end ) embedding of each span is concatenated with the weighted embedding (x i ) and the length feature (φ(i)) to form its final representation e: the span representation of s and p are marked as e s and e p , respectively.