适应类增强原型网络用于几次拍摄关系提取.
Rongzhen Li1, Jiang Zhong1, Wenyue Hu1
1College of Computer Science, Chongqing University, Chongqing 400044, PR China.
概括
几次拍摄的关系提取可以使用自适应类增强原型网络来改进知识构建. 这种方法增强了表示学习,提高了新型关系的准确性和概括性.
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:
- 关系提取对于知识构建至关重要,但需要大量的注释数据.
- 少数拍摄关系提取通过从有限的例子学习来解决数据稀缺问题.
- 现有的方法在与类内/类间差异和有偏见的特征作斗争.
研究的目的:
- 引入适应类增强型原型网络,用于几次拍摄的关系提取.
- 通过加强实例级和表示级特征来增强表示学习.
- 为了提高模型的概括性和对未见关系类的准确性.
主要方法:
- 开发了一种适应类增强机制,用于实例级扩展.
- 实现了类增强表示学习与伯努利扰乱上下文注意.
- 采用适应式非基于的对比式学习来进行模型培训.
主要成果:
- 拟议的网络显著提高了在少数镜头设置中的准确性和概括性.
- 观察到性能提高,特别是在跨领域和具有挑战性的任务中.
- 该模型有效地解决了有偏见的类特征和虚假相关性的问题.
结论:
- 适应类增强型原型网络为少数拍摄关系提取提供了强大的解决方案.
- 增强和减弱技术加强了表示学习.
- 这种方法通过在有限的数据上提高模型适应性和性能来推进该领域.
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