Title: Learning Contextualized Knowledge Structures for Commonsense Reasoning. Abstract: Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference. However, current neural-symbolic reasoning methods rely on quality and contextualized knowledge structures (i.e., fact triples) that can be retrieved at the pre-processing stage and overlook challenges such as dealing with incompleteness of a KG (low coverage), limited expressiveness of its relations, and irrelevant retrieved facts in the reasoning context. 
In this paper, we present a novel neural-symbolic approach, named Hybrid Graph Network (HGN), which jointly generates feature representations for new triples (as complement to the existing edges in the KG), determines relevance of the triples to the reasoning context, and learns graph model parameters for encoding the relational information. Our method learns a compact graph structure (comprising both retrieved and generated edges) through filtering edges that are unhelpful to the reasoning process. We show marked improvements on three commonsense reasoning benchmarks and demonstrate the superiority of the learned graph structures with user studies.