  Dependency trees help relation extraction models capture long-range relations
between words. However, existing dependency-based models either neglect crucial
information (e.g., negation) by pruning the dependency trees too aggressively,
or are computationally inefficient because it is difficult to parallelize over
different tree structures. We propose an extension of graph convolutional
networks that is tailored for relation extraction, which pools information over
arbitrary dependency structures efficiently in parallel. To incorporate
relevant information while maximally removing irrelevant content, we further
apply a novel pruning strategy to the input trees by keeping words immediately
around the shortest path between the two entities among which a relation might
hold. The resulting model achieves state-of-the-art performance on the
large-scale TACRED dataset, outperforming existing sequence and
dependency-based neural models. We also show through detailed analysis that
this model has complementary strengths to sequence models, and combining them
further improves the state of the art.
