  Event-centric structured prediction involves predicting structured outputs of
events. In most NLP cases, event structures are complex with manifold
dependency, and it is challenging to effectively represent these complicated
structured events. To address these issues, we propose Structured Prediction
with Energy-based Event-Centric Hyperspheres (SPEECH). SPEECH models complex
dependency among event structured components with energy-based modeling, and
represents event classes with simple but effective hyperspheres. Experiments on
two unified-annotated event datasets indicate that SPEECH is predominant in
event detection and event-relation extraction tasks.
