KEPT: Knowledge Enhanced Prompt Tuning for event causality identification

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jintao Liu , Zequn Zhang , Zhi Guo , Li Jin , Xiaoyu Li , Kaiwen Wei , Xian Sun
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引用次数: 11

Abstract

Event causality identification (ECI) aims to identify causal relations of event mention pairs in text. Despite achieving certain accomplishments, existing methods are still not effective due to the following two issues: (1) the lack of causal reasoning ability, imposing restrictions on recognizing implicit causal relations; (2) the significant gap between fine-tuning and pre-training, which hinders the utilization of pre-trained language models (PLMs). In this paper, we propose a novel Knowledge Enhanced Prompt Tuning (KEPT) framework for ECI to address the issues mentioned above. Specifically, this method leverages prompt tuning to incorporate two kinds of knowledge obtained from external knowledge bases (KBs), including background information and relational information, for causal reasoning. To introduce external knowledge into our model, we first convert it to textual descriptions, then design an interactive attention mechanism and a selective attention mechanism to fuse background information and relational information, respectively. In addition, to further capture implicit relations between events, we adopt the objective from knowledge representation learning to jointly optimize the representations of causal relations and events. Experiment results on two widely-used benchmarks demonstrate that the proposed method outperforms the state-of-the-art models.

KEPT:事件因果关系识别的知识增强提示调优
事件因果关系识别(ECI)旨在识别文本中事件-提及对的因果关系。尽管取得了一定的成就,但由于以下两个问题,现有的方法仍然不够有效:(1)缺乏因果推理能力,限制了对隐含因果关系的认识;(2) 微调和预训练之间的巨大差距阻碍了预训练语言模型(PLM)的使用。在本文中,我们为ECI提出了一个新的知识增强提示调整(KEPT)框架来解决上述问题。具体而言,该方法利用即时调整来合并从外部知识库(KB)获得的两种知识,包括背景信息和关系信息,用于因果推理。为了将外部知识引入我们的模型,我们首先将其转换为文本描述,然后分别设计了一个交互式注意力机制和一个选择性注意力机制来融合背景信息和关系信息。此外,为了进一步捕捉事件之间的隐含关系,我们采用了知识表征学习的目标,共同优化因果关系和事件的表征。在两个广泛使用的基准测试上的实验结果表明,所提出的方法优于最先进的模型。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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