WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference
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WorldTree：支持多跳推理的基本科学问题解释图语料库
DOI：
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发表时间：
2018
期刊：
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通讯作者：
Clayton T. Morrison
中科院分区：
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作者：
Peter Alexander Jansen;Elizabeth Wainwright;Steven Marmorstein;Clayton T. Morrison
开发自动推理方法，能够为用户提供令人信服的人类可读的理由，说明为什么问题的答案是正确的，这对于科学和医学等领域至关重要，在这些领域，用户信任和检测代价高昂的错误是采用的限制因素。在可解释推理任务上训练问答模型的主要障碍之一是缺乏黄金解释作为训练数据。在本文中，我们提出了一个语料库的解释标准化科学考试，最近的挑战性任务的问题回答。我们手动构建了几乎所有公开的标准化基础科学问题（大约1，680个三年级到五年级的问题）的详细解释的语料库，并将其表示为“解释图”-词汇重叠的句子集，描述如何通过领域和世界知识的组合来获得问题的正确答案。我们还提供了一个以解释为中心的表库，一个包含构建这些基本科学解释的知识的半结构化表的集合。这两个知识资源一起映射出回答和解释小学科学考试所需的大部分知识，并为可解释推理任务提供结构化和自由文本训练数据。
Developing methods of automated inference that are able to provide users with compelling human-readable justifications for why the answer to a question is correct is critical for domains such as science and medicine, where user trust and detecting costly errors are limiting factors to adoption. One of the central barriers to training question answering models on explainable inference tasks is the lack of gold explanations to serve as training data. In this paper we present a corpus of explanations for standardized science exams, a recent challenge task for question answering. We manually construct a corpus of detailed explanations for nearly all publicly available standardized elementary science question (approximately 1,680 3rd through 5th grade questions) and represent these as "explanation graphs" -- sets of lexically overlapping sentences that describe how to arrive at the correct answer to a question through a combination of domain and world knowledge. We also provide an explanation-centered tablestore, a collection of semi-structured tables that contain the knowledge to construct these elementary science explanations. Together, these two knowledge resources map out a substantial portion of the knowledge required for answering and explaining elementary science exams, and provide both structured and free-text training data for the explainable inference task.