Jintao Liu , Zequn Zhang , Zhi Guo , Li Jin , Xiaoyu Li , Kaiwen Wei , Xian Sun
{"title":"KEPT: Knowledge Enhanced Prompt Tuning for event causality identification","authors":"Jintao Liu , Zequn Zhang , Zhi Guo , Li Jin , Xiaoyu Li , Kaiwen Wei , Xian Sun","doi":"10.1016/j.knosys.2022.110064","DOIUrl":null,"url":null,"abstract":"<div><p><span><span>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 </span>language models (PLMs). In this paper, we propose a novel </span><strong>K</strong>nowledge <strong>E</strong>nhanced <strong>P</strong>rompt <strong>T</strong><span><span>uning (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 </span>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.</span></p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"259 ","pages":"Article 110064"},"PeriodicalIF":7.2000,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705122011571","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.