  Multi-hop QA (Question Answering) is the task of finding the answer to a
question across multiple documents. In recent years, a number of Deep
Learning-based approaches have been proposed to tackle this complex task, as
well as a few standard benchmarks to assess models Multi-hop QA capabilities.
In this paper, we focus on the well-established HotpotQA benchmark dataset,
which requires models to perform answer span extraction as well as support
sentence prediction. We present two extensions to the SOTA Graph Neural Network
(GNN) based model for HotpotQA, Hierarchical Graph Network (HGN): (i) we
complete the original hierarchical structure by introducing new edges between
the query and context sentence nodes; (ii) in the graph propagation step, we
propose a novel extension to Hierarchical Graph Attention Network GATH (Graph
ATtention with Hierarchies) that makes use of the graph hierarchy to update the
node representations in a sequential fashion. Experiments on HotpotQA
demonstrate the efficiency of the proposed modifications and support our
assumptions about the effects of model related variables.
