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Explainable question answering for science questions is a challenging taskthat requires multi-hop inference over a large set of fact sentences. Tocounter the limitations of methods that view each query-document pair inisolation, we propose the LSTM-Interleaved Transformer which incorporatescross-document interactions for improved multi-hop ranking. The LITarchitecture can leverage prior ranking positions in the re-ranking setting.Our model is competitive on the current leaderboard for the TextGraphs 2020shared task, achieving a test-set MAP of 0.5607, and would have gained thirdplace had we submitted before the competition deadline. Our code implementationis made available athttps://github.com/mdda/worldtree_corpus/tree/textgraphs_2020

Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking