概念增强的异质图形网络用于事实验证
Zhendong Chen1, Lejian Liao2, Siu Cheung Hui3
1College of Computer Science and Technology, Zhejiang Normal University, China.
概括
本研究介绍了概念增强异质图形网络 (概念-HGN) 用于事实验证,通过整合多颗粒度和概念信息来提高准确性. 这种新的方法在基准数据集上取得了最先进的结果.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 信息检索 信息检索
背景情况:
- 事实验证是一个复杂的NLP任务,需要从可靠的来源获取证据.
- 现有的方法往往忽略了多个细分信息,缺乏固有的概念理解.
- 挑战包括聚合分散的文本线索和利用实体概念以获得准确性.
研究的目的:
- 为改进事实验证提出一个新的概念增强异质图形网络 (概念-HGN).
- 解决现有事实验证模型关于多细分和概念信息的局限性.
- 提高自动化事实核查系统的准确性和稳定性.
主要方法:
- 构建一个异质图,从多个证据句子汇总信息.
- 在图中实现层次节点的细分化,以便有效的线索聚合.
- 利用YAGO的内在实体概念来指导事实验证过程.
主要成果:
- 在FEVER数据集中,概念HGN实现了80.26% (LA) 和77.68% (FS).
- 在UKP Snopes数据集上,准确率达到65.7%,宏 F1达到61.9%.
- 与基线模型相比,拟议的模型表现出优越的性能.
结论:
- 概念-HGN有效地整合了多颗粒度和概念信息,用于事实验证.
- 该模型实现了最先进的性能,超过了现有方法.
- 这种方法为推进自动化事实核查技术提供了一个有希望的方向.
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