基于抽象事理图谱的因果简答题求解方法

陈越,何宇豪,孙亚伟,程龚,瞿裕忠

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中文信息学报 ›› 2022, Vol. 36 ›› Issue (4) : 124-136.
专题: 面向类人智能的教育认知关键技术

基于抽象事理图谱的因果简答题求解方法

  • 陈越,何宇豪,孙亚伟,程龚,瞿裕忠
作者信息 +

Answering Causal Essay Questions Based on Abstract Event Graph

  • CHEN Yue, HE Yuhao, SUN Yawei, CHENG Gong, QU Yuzhong
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摘要

如何利用人工智能技术回答标准测试题目是一项具有挑战性的任务,吸引了人工智能领域的广泛研究。该文聚焦在高中地理的因果简答题求解任务,求解因果简答题需要进行知识集成和多跳因果推理,最终生成一段长文本作为答案。为此,该文定义了抽象事理图谱(AEG)来表示因果等关系,并利用预训练语言模型从语料中自动抽取一个面向高中地理因果简答题的抽象事理图谱,实现了多源知识集成。基于抽象事理图谱,该文利用图神经网络技术来融合结构化和非结构化知识,实现了多跳因果推理。该文在包含真实的高中地理因果简答题的数据集GeoCEQA上开展实验,结果表明,无论是ROUGE、BLEU指标还是人工评价的得分,该文提出的方法都取得了最佳结果,在ROUGE指标上,相比最优基线方法提升0.8%~1.4%;在BLEU指标上,相比最优基线方法提升0.4%;在人工评价得分上,相比最优基线方法提升4.2%。

Abstract

The challenge of creating an AI that can pass standard exams has attracted extensive research attention. In this paper, we focus on answering causal essay questions in high-school geography exams, which requires knowledge integration, multi-hop causal reasoning, and long-text answer generation. To this end, we define abstract event graph (AEG) to represent causal relations, and employ a pre-trained language model to construct an AEG from a corpus to integrate knowledge from multiple sources about high-school geography. Based on AEG, we employ graph neural network to unify structured and unstructured knowledge and realize multi-hop causal reasoning. On the GeoCEQA dataset with real high-school geography causal essay questions, our approach significantly outperforms the best baseline model by 0.8%—1.4% on ROUGE, by 0.4% according to BLEU, and by 4.2% according to human evaluation.

关键词

因果简答题 / 抽象事理图谱 / 多跳因果推理

Key words

causal essay question / abstract event graph / multi-hop causal reasoning

引用本文

导出引用
陈越,何宇豪,孙亚伟,程龚,瞿裕忠. 基于抽象事理图谱的因果简答题求解方法. 中文信息学报. 2022, 36(4): 124-136
CHEN Yue, HE Yuhao, SUN Yawei, CHENG Gong, QU Yuzhong. Answering Causal Essay Questions Based on Abstract Event Graph. Journal of Chinese Information Processing. 2022, 36(4): 124-136

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基金

国家重点研究与发展计划项目(2018YFB1005100)
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