I built a pure-Python port of Stanford OpenIE that GPU-accelerates the natural-logic forward-entailment search itself (via batched reparsing) rather than replacing it with a neural model. This often yields more triplets than standard OpenIE while maintaining the original semantics.

Many neural OpenIE systems train on labels from classical systems. This keeps the classical algorithm but parallelizes the BFS exploration on GPU.