LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification
文献类型:会议论文
作者 | Xinyu Zuo1,2; Pengfei Cao1,2; Yubo Chen1,2; Kang Liu1,2; Jun Zhao1,2; Weihua Peng3; Yuguang Chen3 |
出版日期 | 2021 |
会议日期 | August 1-6, 2021 |
会议地点 | Bangkok, Thailand (Online) |
英文摘要 | Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLPrelated augmentation methods cannot directly produce available data required for this task. To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. On the one hand, our approach is knowledge guided, which can leverage existing knowledge bases to generate well-formed new sentences. On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework, and can interactively adjust the generation process to generate task-related sentences. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related training data for ECI; 2) our method outperforms previous methods on EventStoryLine and Causal TimeBank (+2.5 and +2.1 points on F1 value respectively). |
语种 | 英语 |
源URL | [http://ir.ia.ac.cn/handle/173211/44854] |
专题 | 模式识别国家重点实验室_自然语言处理 |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 3.Beijing Baidu Netcom Science Technology Co., Ltd |
推荐引用方式 GB/T 7714 | Xinyu Zuo,Pengfei Cao,Yubo Chen,et al. LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification[C]. 见:. Bangkok, Thailand (Online). August 1-6, 2021. |
入库方式: OAI收割
来源:自动化研究所
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