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

中文翻译：

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TextGraphs 2021 上的 Red Dragon AI 共享任务：通过匹配专家评级进行多跳推理解释再生

为科学问题的答案创建解释是一项具有挑战性的任务，需要对大量事实句子进行多跳推理。今年，为了将 Textgraphs 共享任务重新聚焦在收集相关陈述的问题上（而不是仅仅寻找单一的“正确路径”），WorldTree 数据集增加了专家对陈述与每个整体解释的“相关性”的评级。我们的系统在共享任务排行榜上获得第二名，结合了初始语句检索；训练语言模型以预测相关性分数；以及一些由此产生的排名的集合。我们的代码实现可在 https://github.com/mdda/worldtree_corpus/tree/textgraphs_2021 获得