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: <meaning-changed>  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 embeddings for the subgraph's missing edges to form a "hybrid" subgraph, and filter out task-irrelevant facts .