一个语法证据网络模型用于事实验证
Zhendong Chen1, Siu Cheung Hui2, Fuzhen Zhuang3
1Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, China; School of Computer Science and Technology, Beijing Institute of Technology, China.
概括
本研究介绍了用于事实验证的语法证据网络 (SENet) 模型. 通过使用语法信息和注意力机制,SENet提高了准确性,以专注于索赔和证据中的相关单词.
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
- 计算语言学 计算语言学
背景情况:
- 事实验证对于使用证据来评估索赔真实性至关重要.
- 当前的深度学习方法经常与不相关的词汇扎,注意事实验证.
- 现有的模型缺乏对重要索赔和证据词语的具体约束.
研究的目的:
- 提出一种新的语法证据网络 (SENet) 模型,以加强事实验证.
- 通过结合语法信息来解决当前注意力机制的局限性.
- 提高自动化事实检查系统的准确性和性能.
主要方法:
- 开发了SENet模型,集成实体关键字,语法信息和句子注意力.
- 使用预先训练的语法依赖性解析器来提取句子结构.
- 将语法信息纳入注意力机制,以实现语言驱动的单词表示.
主要成果:
- 在FEVER数据集上获得了78.69%的标签准确度和75.63%的FEVER分数.
- 在UKP Snopes数据集上获得了65.0%的精度和61.2%的宏 F1.
- 与基线模型相比,在事实验证任务中表现优越.
结论:
- 该SENet模型显著提高了事实验证的准确性.
- 整合语法信息和有针对性的注意力可以改善语义表示.
- SENet实现了最先进的性能,超过了现有的事实核查方法.
关键词:
事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证事实验证实验证事实验证实验证句子注意力机制句子注意力机制语法上的信息是语法上的信息.更多相关视频
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