  Copy mechanisms are employed in sequence to sequence models (seq2seq) to
generate reproductions of words from the input to the output. These frameworks,
operating at the lexical type level, fail to provide an explicit alignment that
records where each token was copied from. Further, they require contiguous
token sequences from the input (spans) to be copied individually. We present a
model with an explicit token-level copy operation and extend it to copying
entire spans. Our model provides hard alignments between spans in the input and
output, allowing for nontraditional applications of seq2seq, like information
extraction. We demonstrate the approach on Nested Named Entity Recognition,
achieving near state-of-the-art accuracy with an order of magnitude increase in
decoding speed.
