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

Utilizing coarse-grained data in low-data settings for event extraction