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