跨语言共享知识的自我监督发现,用于持续的文本识别
概括
本研究介绍了CrossKnow,这是一个用于增量多语言文本识别的新框架. CrossKnow有效地发现和转移跨语言知识,改善持续学习,而不需要每个字符的语言标签.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 增量多语言文本识别 (IMLTR) 面临跨语言知识共享的挑战.
- 目前的IMLTR方法假设单语言域,忽视固有的多语言数据属性.
- 渐进式共享问题,即跨语言知识被忽视,阻碍了持续的学习.
研究的目的:
- 为IMLTR提出一个新的自我监督的跨语言知识发现框架,CrossKnow.
- 解决现有的IMLTR方法在处理多语言场景方面的局限性.
- 通过减轻知识遗忘和实现高效的知识转移来改善IMLTR的持续学习.
主要方法:
- 开发了一个共享的知识发现策略,使用跨识别器的预测一致性.
- 消除了对单个字符的语言标签的依赖.
- 设计了一种多细分,多任务的语言域区分器,以捕捉语言间的依赖关系.
- 使用发现的共享知识引导层次序列解码.
主要成果:
- 交叉知识有效地减轻了以前学到的知识的遗忘.
- 该框架实现了高效的跨语言知识转移.
- 在MLT17和MLT19数据集上的实验表明CrossKnow的优势.
- 在没有额外的语言监督的情况下实现了竞争性表现,减少了存储和提高了效率.
结论:
- CrossKnow显著推进了增量多语言文本识别.
- 拟议的方法提供了一个更准确,更有效的方法,在多语言背景下持续学习.
- 交叉知识证明了自主监督的IMLTR跨语言知识发现的有效性.
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