  Recently, knowledge graph (KG) augmented models have achieved noteworthy
success on various commonsense reasoning tasks. However, KG edge (fact)
sparsity and noisy edge extraction/generation often hinder models from
obtaining useful knowledge to reason over. To address these issues, we propose
a new KG-augmented model: Hybrid Graph Network (HGN). Unlike prior methods, HGN
learns to jointly contextualize extracted and generated knowledge by reasoning
over both within a unified graph structure. Given the task input context and an
extracted KG subgraph, HGN is trained to generate embeddings for the subgraph's
missing edges to form a "hybrid" graph, then reason over the hybrid graph while
filtering out context-irrelevant edges. We demonstrate HGN's effectiveness
through considerable performance gains across four commonsense reasoning
benchmarks, plus a user study on edge validness and helpfulness.
