  We present a novel, language-agnostic approach to "priming" language models
for the task of event extraction, providing particularly effective performance
in low-resource and zero-shot cross-lingual settings. With priming, we augment
the input to the transformer stack's language model differently depending on
the question(s) being asked of the model at runtime. For instance, if the model
is being asked to identify arguments for the trigger "protested", we will
provide that trigger as part of the input to the language model, allowing it to
produce different representations for candidate arguments than when it is asked
about arguments for the trigger "arrest" elsewhere in the same sentence. We
show that by enabling the language model to better compensate for the deficits
of sparse and noisy training data, our approach improves both trigger and
argument detection and classification significantly over the state of the art
in a zero-shot cross-lingual setting.
