  Data scarcity has been the main factor that hinders the progress of event
extraction. To overcome this issue, we propose a Self-Training with Feedback
(STF) framework that leverages the large-scale unlabeled data and acquires
feedback for each new event prediction from the unlabeled data by comparing it
to the Abstract Meaning Representation (AMR) graph of the same sentence.
Specifically, STF consists of (1) a base event extraction model trained on
existing event annotations and then applied to large-scale unlabeled corpora to
predict new event mentions as pseudo training samples, and (2) a novel scoring
model that takes in each new predicted event trigger, an argument, its argument
role, as well as their paths in the AMR graph to estimate a compatibility score
indicating the correctness of the pseudo label. The compatibility scores
further act as feedback to encourage or discourage the model learning on the
pseudo labels during self-training. Experimental results on three benchmark
datasets, including ACE05-E, ACE05-E+, and ERE, demonstrate the effectiveness
of the STF framework on event extraction, especially event argument extraction,
with significant performance gain over the base event extraction models and
strong baselines. Our experimental analysis further shows that STF is a generic
framework as it can be applied to improve most, if not all, event extraction
models by leveraging large-scale unlabeled data, even when high-quality AMR
graph annotations are not available.
