  In this paper, we aim to extract commonsense knowledge to improve machine
reading comprehension. We propose to represent relations implicitly by
situating structured knowledge in a context instead of relying on a pre-defined
set of relations, and we call it contextualized knowledge. Each piece of
contextualized knowledge consists of a pair of interrelated verbal and
nonverbal messages extracted from a script and the scene in which they occur as
context to implicitly represent the relation between the verbal and nonverbal
messages, which are originally conveyed by different modalities within the
script. We propose a two-stage fine-tuning strategy to use the large-scale
weakly-labeled data based on a single type of contextualized knowledge and
employ a teacher-student paradigm to inject multiple types of contextualized
knowledge into a student machine reader. Experimental results demonstrate that
our method outperforms a state-of-the-art baseline by a 4.3% improvement in
accuracy on the machine reading comprehension dataset C^3, wherein most of the
questions require unstated prior knowledge.
