From Simple to Complex: A Progressive Framework for Document-level
  Informative Argument Extraction
Quzhe Huang Yanxi Zhang Dongyan Zhao
Document-level Event Argument Extraction (EAE) requires the model to extract
arguments of multiple events from a single document. Considering the underlying
dependencies between these events, recent efforts leverage the idea of
"memory", where the results of already predicted events are cached and can be
retrieved to help the prediction of upcoming events. These methods extract
events according to their appearance order in the document, however, the event
that appears in the first sentence does not mean that it is the easiest to
extract. Existing methods might introduce noise to the extraction of upcoming
events if they rely on an incorrect prediction of previous events. In order to
provide more reliable memory, we propose a simple-to-complex progressive
framework for document-level EAE. Specifically, we first calculate the
difficulty of each event and then, we conduct the extraction following a
simple-to-complex order. In this way, the memory will store the most certain
results, and the model could use these reliable sources to help the prediction
of more difficult events. Experiments on WikiEvents show that our model
outperforms SOTA by 1.4% in F1, indicating the proposed simple-to-complex
framework is useful in the EAE task.