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首页> 外文会议>International Conference on Language Resources and Evaluation >WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference
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WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference

机译：WorldTree V2：支持多跳推理的科学域结构解释和推理模式的语音

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摘要

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.
机译：解释复杂问题的解释答复通常需要组合大量的事实来回答问题，同时为答案提供人类可读的解释，称为多跳推断的过程。标准化的科学问题需要平均6个事实，以及多达16个事实，以回答和解释，但是多跳推理的大多数现有数据集专注于组合两个事实，显着限制了多跳推论的能力学习生成大推论的算法。在这项工作中，我们展示了WorldTree项目的第二次迭代，这是一个5,114标准化科学考试问题的语料库，与核心科学知识和世界知识相结合的大型详细的多事实解释。每个解释都表示为词汇连接的“解释图”，其与在66个表中的9,216个事实的半结构化知识库中绘制的平均值的平均值相结合。我们使用此解释语料库来创作一组344个高级科学域推理模式，类似于支持多跳推断的语义帧。这些资源在一起提供了培训数据和仪器，用于开发诸多事实的多跳推理模型进行问题应答。

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