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Creating explanations for answers to science questions is a challenging taskthat requires multi-hop inference over a large set of fact sentences. Thisyear, to refocus the Textgraphs Shared Task on the problem of gatheringrelevant statements (rather than solely finding a single 'correct path'), theWorldTree dataset was augmented with expert ratings of 'relevance' ofstatements to each overall explanation. Our system, which achieved second placeon the Shared Task leaderboard, combines initial statement retrieval; languagemodels trained to predict the relevance scores; and ensembling of a number ofthe resulting rankings. Our code implementation is made available athttps://github.com/mdda/worldtree_corpus/tree/textgraphs_2021

Red Dragon AI at TextGraphs 2021 Shared Task: Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings