  In this work we describe the system from Natural Language Processing group at
Arizona State University for the TextGraphs 2019 Shared Task. The task focuses
on Explanation Regeneration, an intermediate step towards general multi-hop
inference on large graphs. Our approach consists of modeling the explanation
regeneration task as a \textit{learning to rank} problem, for which we use
state-of-the-art language models and explore dataset preparation techniques. We
utilize an iterative re-ranking based approach to further improve the rankings.
Our system secured 2nd rank in the task with a mean average precision (MAP) of
41.3\% on the test set.
