before_revision: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks , like question answering (QA) . However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge . To address these issues, we propose  Hybrid Graph Network (HGN) , a neural-symbolic model that reasons over both extracted (human-labeled) and generated facts within the same learned graph structure. Given a KG subgraphof extracted facts , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts . We demonstrate HGN's ability to produce contextually pertinent subgraphs by showing considerable performance gains across four commonsense reasoning benchmarks and a user study of fact validness and helpfulness.
after_revision: 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.
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edit_actions: , a neural-symbolic model that reasons over both extracted (human-labeled) and generated facts within the same learned
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