科学问题解释的统一重建（CS AI）---用户7095611

本文提出了一个通过以解释为中心的语料库重构多项选择科学问题解释的框架。在科学统一概念的基础上，该框架利用两个不同分数的组合，对问题和候选答案的解释性事实进行排序：（a）相关分数（RS），表示给定事实对问题的具体程度；（b） 一种考虑到一个事实的解释力的统一分数（US），根据其解释相似问题的频率来确定。在Worldtree语料库上对该框架进行了广泛的评价，并采用了IR加权方案来实现。研究结果如下：（1）与目前最先进的变压器相比，该方法具有可扩展到大型解释性知识库的特点；（2）组合模型明显优于IR基线（+7.8/8.4map），确定相关性和统一性评分的互补性；（3）构建的解释支持下游模型进行答案预测，提高了ARC-easy（+6.92%）和challenge（+15.69%）两个问题的多选择问答的BERT准确性。

原文题目：Unification-based Reconstruction of Explanations for Science Questions

原文：The paper presents a framework to reconstruct explanations for multiple choices science questions through explanation-centred corpora. Building upon the notion of unification in science, the framework ranks explanatory facts with respect to question and candidate answer by leveraging a combination of two different scores: (a) A Relevance Score (RS) that represents the extent to which a given fact is specific to the question; (b) A Unification Score (US) that takes into account the explanatory power of a fact, determined according to its frequency in explanations for similar questions. An extensive evaluation of the framework is performed on the Worldtree corpus, adopting IR weighting schemes for its implementation. The following findings are presented: (1) The proposed approach achieves competitive results when compared to state-of-the-art Transformers, yet possessing the property of being scalable to large explanatory knowledge bases; (2) The combined model significantly outperforms IR baselines (+7.8/8.4 MAP), confirming the complementary aspects of relevance and unification score; (3) The constructed explanations can support downstream models for answer prediction, improving the accuracy of BERT for multiple choices QA on both ARC easy (+6.92%) and challenge (+15.69%) questions.

原文作者：Marco Valentino

原文地址：https://arxiv.org/abs/2004.00061