零射击跨语言立场检测通过对抗性语言适应
1Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, India.
PeerJ. Computer science
|September 24, 2025
概括
这项研究引入了一种新的零射击跨语言立场检测方法,增强跨语言的疫苗立场分类,没有针对特定目标的培训数据. 该方法使用翻译增强和对抗性学习来提高性能.
科学领域:
- 自然语言处理自然语言处理.
- 计算社会科学 计算社会科学
背景情况:
- 姿势检测通常集中在单个语言上.
- 现有的跨语言方法往往需要少量学习,这限制了它们的适用性.
- 开发零射击跨语言模型带来了重大挑战.
研究的目的:
- 引入一种用于零射击跨语言姿势检测的新方法.
- 在没有目标语言培训数据的情况下,提高跨语言立场分类器的性能.
- 为了解决跨语言立场检测当前研究的局限性.
主要方法:
- 提出了一种新的方法:多语言翻译增强来自变压器 (MTAB) 的双向编码器表示.
- 使用翻译增强来提高零射击性能.
- 综合对抗式学习进一步提高模型的有效性.
主要成果:
- 在英语,德语,法语和意大利语的疫苗立场检测数据集上证明了MTAB的有效性.
- 与强大的基线和废弃模型版本相比,展示了改进的结果.
- 验证了翻译增强数据和对抗性学习对模型性能的贡献.
结论:
- 拟议的MTAB方法显著改善了零射击跨语言立场检测.
- 翻译增强和对抗式学习是增强跨语言分类的有效组成部分.
- 该研究为多语言NLP和社交媒体分析做出了有价值的贡献.
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