  We introduce a general framework for several information extraction tasks
that share span representations using dynamically constructed span graphs. The
graphs are constructed by selecting the most confident entity spans and linking
these nodes with confidence-weighted relation types and coreferences. The
dynamic span graph allows coreference and relation type confidences to
propagate through the graph to iteratively refine the span representations.
This is unlike previous multi-task frameworks for information extraction in
which the only interaction between tasks is in the shared first-layer LSTM. Our
framework significantly outperforms the state-of-the-art on multiple
information extraction tasks across multiple datasets reflecting different
domains. We further observe that the span enumeration approach is good at
detecting nested span entities, with significant F1 score improvement on the
ACE dataset.
