  This work deals with the challenge of learning and reasoning over multi-hop
question answering (QA). We propose a graph reasoning network based on the
semantic structure of the sentences to learn cross paragraph reasoning paths
and find the supporting facts and the answer jointly. The proposed graph is a
heterogeneous document-level graph that contains nodes of type sentence
(question, title, and other sentences), and semantic role labeling sub-graphs
per sentence that contain arguments as nodes and predicates as edges.
Incorporating the argument types, the argument phrases, and the semantics of
the edges originated from SRL predicates into the graph encoder helps in
finding and also the explainability of the reasoning paths. Our proposed
approach shows competitive performance on the HotpotQA distractor setting
benchmark compared to the recent state-of-the-art models.
