  Commonsense question answering aims to answer questions which require
background knowledge that is not explicitly expressed in the question. The key
challenge is how to obtain evidence from external knowledge and make
predictions based on the evidence. Recent works either learn to generate
evidence from human-annotated evidence which is expensive to collect, or
extract evidence from either structured or unstructured knowledge bases which
fails to take advantages of both sources. In this work, we propose to
automatically extract evidence from heterogeneous knowledge sources, and answer
questions based on the extracted evidence. Specifically, we extract evidence
from both structured knowledge base (i.e. ConceptNet) and Wikipedia plain
texts. We construct graphs for both sources to obtain the relational structures
of evidence. Based on these graphs, we propose a graph-based approach
consisting of a graph-based contextual word representation learning module and
a graph-based inference module. The first module utilizes graph structural
information to re-define the distance between words for learning better
contextual word representations. The second module adopts graph convolutional
network to encode neighbor information into the representations of nodes, and
aggregates evidence with graph attention mechanism for predicting the final
answer. Experimental results on CommonsenseQA dataset illustrate that our
graph-based approach over both knowledge sources brings improvement over strong
baselines. Our approach achieves the state-of-the-art accuracy (75.3%) on the
CommonsenseQA leaderboard.
