  Event argument extraction (EAE) has been well studied at the sentence level
but under-explored at the document level. In this paper, we study to capture
event arguments that actually spread across sentences in documents. Prior works
usually assume full access to rich document supervision, ignoring the fact that
the available argument annotation is usually limited. To fill this gap, we
present FewDocAE, a Few-Shot Document-Level Event Argument Extraction
benchmark, based on the existing document-level event extraction dataset. We
first define the new problem and reconstruct the corpus by a novel N -Way-D-Doc
sampling instead of the traditional N -Way-K-Shot strategy. Then we adjust the
current document-level neural models into the few-shot setting to provide
baseline results under in- and cross-domain settings. Since the argument
extraction depends on the context from multiple sentences and the learning
process is limited to very few examples, we find this novel task to be very
challenging with substantively low performance. Considering FewDocAE is closely
related to practical use under low-resource regimes, we hope this benchmark
encourages more research in this direction. Our data and codes will be
available online.
