WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference
WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference
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WorldTree V2：支持多跳推理的科学领域结构化解释和推理模式语料库
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Peter Alexander Jansen
中科院分区：
文献类型：
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作者：
Zhengnan Xie;Sebastian Thiem;Jaycie Martin;Elizabeth Wainwright;Steven Marmorstein;Peter Alexander Jansen
对于复杂的问题，可解释的问题回答通常需要结合大量的事实来回答一个问题，同时为答案提供一个人类可读的解释，这个过程被称为多跳推理。标准化的科学问题需要结合平均6个事实，最多16个事实，才能回答和解释，但大多数现有的多跳推理数据集只关注结合两个事实，这极大地限制了多跳推理算法学习生成大推理的能力。在这项工作中，我们提出了WorldTree项目的第二次迭代，这是一个包含5,114个标准化科学考试问题的语料库，结合了核心科学知识和世界知识的大量详细的多事实解释。每个解释都表示为一个词汇连接的“解释图”，该图结合了从66个表的9,216个事实的半结构化知识库中提取的平均6个事实。我们使用这个解释语料库创建了一组344个高级科学领域推理模式，类似于支持多跳推理的语义框架。总之，这些资源为开发用于问答的多事实多跳推理模型提供了训练数据和工具。
Explainable question answering for complex questions often requires combining large numbers of facts to answer a question while providing a human-readable explanation for the answer, a process known as multi-hop inference. Standardized science questions require combining an average of 6 facts, and as many as 16 facts, in order to answer and explain, but most existing datasets for multi-hop reasoning focus on combining only two facts, significantly limiting the ability of multi-hop inference algorithms to learn to generate large inferences. In this work we present the second iteration of the WorldTree project, a corpus of 5,114 standardized science exam questions paired with large detailed multi-fact explanations that combine core scientific knowledge and world knowledge. Each explanation is represented as a lexically-connected “explanation graph” that combines an average of 6 facts drawn from a semi-structured knowledge base of 9,216 facts across 66 tables. We use this explanation corpus to author a set of 344 high-level science domain inference patterns similar to semantic frames supporting multi-hop inference. Together, these resources provide training data and instrumentation for developing many-fact multi-hop inference models for question answering.
DOI：
10.18653/v1/n19-1405
发表时间：
2019
期刊：
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影响因子：
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作者：
Jifan Chen;Greg Durrett
通讯作者：
Jifan Chen;Greg Durrett