利用符号知识基础来进行常识自然语言推理,使用模式理论.
概括
本研究引入了使用符号知识库和神经网络的混合方法,以减少在常识自然语言推断 (CNLI) 任务中需要标记数据的需求. 该方法以最小的标记数据实现了显著的性能,超过了传统模型.
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:
- 常识自然语言推断 (CNLI) 任务需要大量的标记数据来进行有效的转移学习.
- 现有的CNLI模型适应方法是数据密集型的,限制了它们在低资源场景中的应用.
研究的目的:
- 开发一个新的框架,减少对CNLI任务的注释培训数据的依赖.
- 在CNLI的教师-学生模型中利用象征性的知识基础来提炼知识.
主要方法:
- 提出了一个混合符号神经推理框架,使用ConceptNet作为符号教师和CNLI模型作为学生.
- 弱标记的数据是从未标记的数据中生成的,使用基于格兰纳德模式理论的绑架推理框架.
- 知识蒸是使用弱标记数据和CNLI模型转移学习的标记数据的一小部分来应用的.
主要成果:
- 该方法在没有标记数据的情况下达到63%的最高性能,在有1000个标记样本时达到72%.
- 模式理论框架表现出强大的独立推理能力,优于OpenBookQA上的GPT和BERT等模型.
- 该框架可以概括为无监督和半监督的学习环境,其表现优于现有的基线.
结论:
- 拟议的混合蒸方法显著减少了在CNLI任务中需要标记数据的需求.
- 该框架提供了一种灵活有效的NLP知识转移方法,可适应各种下游任务.
- 该方法通过生成的解释提高了模型的可解释性,为推理过程提供了洞察力.
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