“概念体系描述概念间上下文关系并组织为层次结构,是一类重要的知识资源。本文研究概念体系的自动构建技术,致力于把一个给定的概念集合(词语集合)按照上下位关系,组织成树状结构的概念体系(概念树)。传统做法将概念体系构建任务分解为概念间上下位语义关系判断及概念层次结构构建这两个独立的子任务。两个子任务缺乏信息反馈,容易造成错误累积等问题。近年来,越来越多任务使用预训练语言模型获取词语的语义特征并判断词语间的语义关系,虽然在概念体系构建中取得了一定效果,但是这类做法只能建模第一个子任务,依然存在错误累计等问题。为了解决分步式方法存在的错误累计问题并有效获取词语及其关系语义特征,本文提出一种基于预训练语言模型的端到端概念体系构建方法,一方面利用预训练语言模型获取概念及其上下位关系的语义信息和部分概念体系结构的结构信息,另一方面利用强化学习端到端地建模概念关系判断和完整体系结构的生成。在WordNet数据集上的实验表明,本文所提方法能取得了良好效果,同等条件下,我们的F1值比最好的模型有7.3%的相对提升。”

Few-shot text matching is a more practical technique in natural language processing (NLP) to determine whether two texts are semantically identical. They primarily design patterns to reformulate text matching into a pre-trained task with uniform prompts across all instances. But they fail to take into account the connection between prompts and instances. This paper argues that dynamically strengthening the correlation between particular instances and the prompts is necessary because fixed prompts cannot adequately fit all diverse instances in inference. We suggest IGATE: Instance-Guided prompt leArning for few-shoT tExt matching, a novel pluggable prompt learning method. The gate mechanism used by IGATE, which is between the embedding and the PLM encoders, makes use of the semantics of instances to regulate the effects of the gate on the prompt tokens. The experimental findings show that IGATE achieves SOTA performance on MRPC and QQP, outperforming strong baselines. GitHub will host the release of codes.