mlsanityData to 13 Oct 2026

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

arXiv 2107.13031 ↗First posted 27 Jul 2021cs.CL cs.AI cs.IRcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 27 Jul 2021.

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References · 14

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2 more
  1. 2019Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generationcited by 3
  2. 2020Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Rankingcited by 2

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