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

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