before_sent:  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 .
before_sent_with_intent: <clarity>  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 .
after_sent:  Given a KG subgraphof extracted facts , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" graph, then reason over the hybrid graph while filtering out context-irrelevant edges .