信息理论的补充提示,以改善不断的文本分类
Duzhen Zhang1, Yong Ren2, Chenxing Li2
1Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates.
概括
本研究介绍了用于连续文本分类的信息理论补充提示符 (InfoComp). 信息计算机有效地减轻了灾难性的遗忘,并通过学习不同的任务特定和任务无关的知识空间来增强知识传输.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 持续文本分类 (CTC) 解决了随着时间的推移对不断变化的文本数据进行分类的挑战.
- 现有的CTC方法往往忽视了共享的关键作用,任务无关的知识.
- 灾难性遗忘仍然是连续学习任务中的一个重大障碍.
研究的目的:
- 引入一种新的方法,即信息理论补充提示符 (InfoComp),用于持续的文本分类.
- 通过明确学习特定任务和任务无关的知识来解决现有方法的局限性.
- 通过利用互补学习系统理论,实现无需数据重复的顺序学习.
主要方法:
- InfoComp学习两个不同的提示空间:P ((私有) -提示任务特定知识的提示和S ((共享) -提示任务不变知识的提示.
- 一个信息理论框架最大限度地提高了参数之间的相互信息,以指导快速学习.
- 设计了两个新的损失功能,以加强特定任务的知识积累和增强任务不变的知识保留.
主要成果:
- 信息计算机有效地缓解了以前获得的知识的灾难性遗忘.
- 该方法通过保留任务不变的知识来证明了改进的前知识传输.
- 对各种CTC基准的实验表明,InfoComp的表现优于最先进的方法.
结论:
- 通过平衡任务特定和任务无关的知识学习,InfoComp为持续的文本分类提供了一个有希望的解决方案.
- 该方法可以实现高效的顺序学习,而不需要重复数据.
- 通过提供更强大和可转移的知识获取框架,InfoComp推动了持续学习领域的发展.
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