  We present an empirical study on methods for span finding, the selection of
consecutive tokens in text for some downstream tasks. We focus on approaches
that can be employed in training end-to-end information extraction systems, and
find there is no definitive solution without considering task properties, and
provide our observations to help with future design choices: 1) a tagging
approach often yields higher precision while span enumeration and boundary
prediction provide higher recall; 2) span type information can benefit a
boundary prediction approach; 3) additional contextualization does not help
span finding in most cases.
