  Annotating text data for event information extraction systems is hard,
expensive, and error-prone. We investigate the feasibility of integrating
coarse-grained data (document or sentence labels), which is far more feasible
to obtain, instead of annotating more documents. We utilize a multi-task model
with two auxiliary tasks, document and sentence binary classification, in
addition to the main task of token classification. We perform a series of
experiments with varying data regimes for the aforementioned integration.
Results show that while introducing extra coarse-grained data offers greater
improvement and robustness, a gain is still possible with only the addition of
negative documents that have no information on any event.
