  Commonsense explanation generation aims to empower the machine's sense-making
capability by generating plausible explanations to statements against
commonsense. While this task is easy to human, the machine still struggles to
generate reasonable and informative explanations. In this work, we propose a
method that first extracts the underlying concepts which are served as
\textit{bridges} in the reasoning chain and then integrates these concepts to
generate the final explanation. To facilitate the reasoning process, we utilize
external commonsense knowledge to build the connection between a statement and
the bridge concepts by extracting and pruning multi-hop paths to build a
subgraph. We design a bridge concept extraction model that first scores the
triples, routes the paths in the subgraph, and further selects bridge concepts
with weak supervision at both the triple level and the concept level. We
conduct experiments on the commonsense explanation generation task and our
model outperforms the state-of-the-art baselines in both automatic and human
evaluation.
