基于细粒度强化学习增强噪声数据的低资源跨语言摘要

黄于欣; 顾怀领; 余正涛; 高玉梦; 潘通; 徐佳龙

doi:10.1631/FITEE.2300296

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基于细粒度强化学习增强噪声数据的低资源跨语言摘要

专辑 | Updated：2024-02-06

- 基于细粒度强化学习增强噪声数据的低资源跨语言摘要
  Enhanced Publication
- Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning
- 信息与电子工程前沿（英文） 2024年25卷第1期页码：121-134
- Affiliations：
  
  1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China
  2.Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650504, China
- Author bio：
  
  †E-mail: huangyuxin2004@163.com
  ‡ Corresponding author
- Funds：
  
  National Natural Science Foundation of China(U21B2027;62266027;61972186;62241604);Yunnan Provincial Major Science and Technology Special Plan Projects, China(202302AD080003;202103AA080015;202202AD080003);General Projects of Basic Research in Yunnan Province, China(202301AT070471;202301AT070393);Kunming University of Science and Technology "Double First-Class" Joint Project, China(202201BE070001-021)
- DOI：10.1631/FITEE.2300296
  中图分类号： TP391
- 纸质出版日期：2024-01-0 ，
  
  网络出版日期：2023-12-27，
  
  收稿日期：2023-04-27，
  
  录用日期：2023-10-22
- Accepted：
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黄于欣, 顾怀领, 余正涛, 等. 基于细粒度强化学习增强噪声数据的低资源跨语言摘要[J]. 信息与电子工程前沿（英文）, 2024,25(1):121-134.

YUXIN HUANG, HUAILING GU, ZHENGTAO YU, et al. Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning. [J]. Frontiers of information technology & electronic engineering, 2024, 25(1): 121-134.
黄于欣, 顾怀领, 余正涛, 等. 基于细粒度强化学习增强噪声数据的低资源跨语言摘要[J]. 信息与电子工程前沿（英文）, 2024,25(1):121-134. DOI： 10.1631/FITEE.2300296.

YUXIN HUANG, HUAILING GU, ZHENGTAO YU, et al. Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning. [J]. Frontiers of information technology & electronic engineering, 2024, 25(1): 121-134. DOI： 10.1631/FITEE.2300296.

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基于细粒度强化学习增强噪声数据的低资源跨语言摘要

Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning

DOI：10.1631/FITEE.2300296