  Multiparty Dialogue Machine Reading Comprehension (MRC) differs from
traditional MRC as models must handle the complex dialogue discourse structure,
previously unconsidered in traditional MRC. To fully exploit such discourse
structure in multiparty dialogue, we present a discourse-aware dialogue graph
neural network, DADgraph, which explicitly constructs the dialogue graph using
discourse dependency links and discourse relations. To validate our model, we
perform experiments on the Molweni corpus, a large-scale MRC dataset built over
multiparty dialogue annotated with discourse structure. Experiments on Molweni
show that our discourse-aware model achieves statistically significant
improvements compared against strong neural network MRC baselines.
