  The problem of answering questions using knowledge from pre-trained language
models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA
context (question and answer choice), methods need to (i) identify relevant
knowledge from large KGs, and (ii) perform joint reasoning over the QA context
and KG. Here we propose a new model, QA-GNN, which addresses the above
challenges through two key innovations: (i) relevance scoring, where we use LMs
to estimate the importance of KG nodes relative to the given QA context, and
(ii) joint reasoning, where we connect the QA context and KG to form a joint
graph, and mutually update their representations through graph-based message
passing. We evaluate QA-GNN on the CommonsenseQA and OpenBookQA datasets, and
show its improvement over existing LM and LM+KG models, as well as its
capability to perform interpretable and structured reasoning, e.g., correctly
handling negation in questions.
