StaRS:学习一个稳定的表示空间,用于连续关系分类
IEEE transactions on neural networks and learning systems
|August 23, 2024
概括
本研究引入了一种新的两阶段方法来改进连续关系分类 (CRC) 模型. 通过在适应过程中稳定表示空间和平衡决策边界,该方法提高了知识图构建的性能.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 关系分类 (RC) 对于自动知识图构造至关重要.
- 持续学习设置越来越多地用于RC,因为关系类型不断变化.
- 现有的方法在持续RC (CRC) 中的适应过程中与表示空间扭曲作斗争.
研究的目的:
- 为了解决连续关系分类中的表示空间扭曲.
- 提高CRC模型在动态环境中的稳定性和性能.
- 为持续关系分类开发一个强大的两阶段培训范式.
主要方法:
- 提出了两阶段的培训策略,包括知识蒸和利损失,以实现稳定的适应.
- 在第二阶段引入了自我对比的学习目标,以平衡决策边界.
- 专注于在适应新关系期间保持代表空间的稳定性.
主要成果:
- 拟议的模型在各种连续关系分类设置中,与现有方法相比,表现优越.
- 实验结果验证了知识蒸,边际损失和自我对比学习组件的有效性.
- 量身定制的设计成功地在连续关系分类中实现了更好的性能.
结论:
- 开发的两阶段方法有效地减轻了连续关系分类中的灾难性遗忘.
- 该方法实现了稳定的表示空间,这对于在动态设置中编码实例至关重要.
- 这项工作为知识图表构建的持续学习提供了重大进展.
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