  Interactive machine reading comprehension (iMRC) is machine comprehension
tasks where knowledge sources are partially observable. An agent must interact
with an environment sequentially to gather necessary knowledge in order to
answer a question. We hypothesize that graph representations are good inductive
biases, which can serve as an agent's memory mechanism in iMRC tasks. We
explore four different categories of graphs that can capture text information
at various levels. We describe methods that dynamically build and update these
graphs during information gathering, as well as neural models to encode graph
representations in RL agents. Extensive experiments on iSQuAD suggest that
graph representations can result in significant performance improvements for RL
agents.
