跨语言的通用依赖解析只从一个单语言树木库
概括
本研究介绍了一个跨语言的通用依赖 (UD) 解析框架. 它可以将解析器转移到缺乏树木库的语言,通过多任务学习和自我训练实现近乎原生性能.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 语法解析依赖于树木库,它们通过人类注释来创建是昂贵的.
- 获得每个语言的树是不切实际的,阻碍了跨语言的NLP研究.
研究的目的:
- 开发一个有效的跨语言通用依赖 (UD) 解析框架.
- 为了使解析器从单一源树库转移到缺乏树库的目标语言.
主要方法:
- 通过将两个语言建模任务纳入依赖性解析培训,利用多任务学习.
- 采用了一种自我培训策略,利用未标记的目标语言数据和源树库.
- 实施了英语,中文和29个UD树林的框架.
主要成果:
- 在所有测试的目标语言中实现了有希望的跨语言解析准确性.
- 拟议的方法的性能接近于在特定目标树上训练的解析器的性能.
- 证明了多任务学习和自我训练对低资源跨语言解析的有效性.
结论:
- 开发的框架为低资源的跨语言语法解析提供了可行的解决方案.
- 跨语言转移学习显著减少了对昂贵的,特定语言的注释数据的依赖.
- 这种方法促进了对更广泛的语言进行NLP工具的开发.
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