  CommonsenseQA is a task in which a correct answer is predicted through
commonsense reasoning with pre-defined knowledge. Most previous works have
aimed to improve the performance with distributed representation without
considering the process of predicting the answer from the semantic
representation of the question. To shed light upon the semantic interpretation
of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graph
is pruned from a full integrated graph encompassing Abstract Meaning
Representation (AMR) graph generated from input questions and an external
commonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploited
to interpret the reasoning path as well as to predict the correct answer on the
CommonsenseQA task. This paper presents the manner in which the commonsense
reasoning process can be interpreted with the relations and concepts provided
by the ACP graph. Moreover, ACP-based models are shown to outperform the
baselines.
