Red Dragon AI at TextGraphs 2021 Shared Task: Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings. (arXiv:2107.13031v1 [cs.CL])

Creating explanations for answers to science questions is a challenging task
that requires multi-hop inference over a large set of fact sentences. This
year, to refocus the Textgraphs Shared Task on the problem of gathering
relevant statements (rather than solely finding a single ‘correct path’), the
WorldTree dataset was augmented with expert ratings of ‘relevance’ of
statements to each overall explanation. Our system, which achieved second place
on the Shared Task leaderboard, combines initial statement retrieval; language
models trained to predict the relevance scores; and ensembling of a number of
the resulting rankings. Our code implementation is made available at
https://github.com/mdda/worldtree_corpus/tree/textgraphs_2021