  This paper focuses on how to take advantage of external relational knowledge
to improve machine reading comprehension (MRC) with multi-task learning. Most
of the traditional methods in MRC assume that the knowledge used to get the
correct answer generally exists in the given documents. However, in real-world
task, part of knowledge may not be mentioned and machines should be equipped
with the ability to leverage external knowledge. In this paper, we integrate
relational knowledge into MRC model for commonsense reasoning. Specifically,
based on a pre-trained language model (LM). We design two auxiliary
relation-aware tasks to predict if there exists any commonsense relation and
what is the relation type between two words, in order to better model the
interactions between document and candidate answer option. We conduct
experiments on two multi-choice benchmark datasets: the SemEval-2018 Task 11
and the Cloze Story Test. The experimental results demonstrate the
effectiveness of the proposed method, which achieves superior performance
compared with the comparable baselines on both datasets.
