中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Learning policy scheduling for text augmentation

文献类型：期刊论文

作者Li, Shuokai; Ao, Xiang3; Pan, Feiyang; He, Qing
刊名NEURAL NETWORKS
出版日期2022
卷号145页码:121-127
关键词Data augmentation Text classification
ISSN号0893-6080
DOI10.1016/j.neunet.2021.09.028
英文摘要When training deep learning models, data augmentation is an important technique to improve the performance and alleviate overfitting. In natural language processing (NLP), existing augmentation methods often use fixed strategies. However, it might be preferred to use different augmentation policies in different stage of training, and different datasets may require different augmentation policies. In this paper, we take dynamic policy scheduling into consideration. We design a search space over augmentation policies by integrating several common augmentation operations. Then, we adopt a population based training method to search the best augmentation schedule. We conduct extensive experiments on five text classification and two machine translation tasks. The results show that the optimized dynamic augmentation schedules achieve significant improvements against previous methods. (C) 2021 Elsevier Ltd. All rights reserved.
资助项目National Key Research and De-velopment Program of China[2017YFB1002104] ; National Natural Science Foundation of China[92046003] ; National Natural Science Foundation of China[61976204] ; National Natural Science Foundation of China[U1811461] ; Project of Youth Innovation Promotion Association CAS ; Beijing Nova Program[Z201100006820062] ; Ant Financial through the Ant Financial Science Funds for Security Research
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000717665500006
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/18111]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ao, Xiang
作者单位1.Inst Intelligent Comp Technol, Suzhou, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Sci, CAS, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Shuokai,Ao, Xiang,Pan, Feiyang,et al. Learning policy scheduling for text augmentation[J]. NEURAL NETWORKS,2022,145:121-127.
APA Li, Shuokai,Ao, Xiang,Pan, Feiyang,&He, Qing.(2022).Learning policy scheduling for text augmentation.NEURAL NETWORKS,145,121-127.
MLA Li, Shuokai,et al."Learning policy scheduling for text augmentation".NEURAL NETWORKS 145(2022):121-127.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。