Knowledge Commons of Institute of Automation,CAS
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision | |
Xinyu Zuo1,2![]() ![]() ![]() ![]() | |
2020 | |
会议名称 | Proceedings of the 28th International Conference on Computational Linguistics |
页码 | 1544–1550 |
会议日期 | December 8-13, 2020 |
会议地点 | Barcelona, Spain (Online) |
摘要 | Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and CausalTimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data. |
学科门类 | 工学 ; 工学::计算机科学与技术(可授工学、理学学位) |
URL | 查看原文 |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[61533018] ; National Natural Science Foundation of China[61806201] |
语种 | 英语 |
七大方向——子方向分类 | 自然语言处理 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44829 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Xinyu Zuo,Yubo Chen,Kang Liu,et al. KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision[C],2020:1544–1550. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
COLING2020-342-Camer(769KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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