论文标题
采矿逻辑事件模式来自预训练的语言模型
Mining Logical Event Schemas From Pre-Trained Language Models
论文作者
论文摘要
我们提出了NESL（神经剧本模式学习者），这是一个事件模式学习系统，结合了大型语言模型，Framenet解析，强大的语言逻辑表示以及一组简单的行为模式，旨在引导学习过程。我们的数据集代替了预先制作的故事，是预先训练的语言模型中的“情况样本”的连续饲料，然后将其解析为Framenet框架，映射到简单的行为模式中，并将其合并成复杂的，概括为复杂的，等级制度，以获取各种日常情况。我们表明，从语言模型中进行仔细的采样可以帮助强调情况的刻板印象，并逐渐强调无关紧要的细节，并且所产生的模式比其他系统所学的模式更全面地指定了情况。
We present NESL (the Neuro-Episodic Schema Learner), an event schema learning system that combines large language models, FrameNet parsing, a powerful logical representation of language, and a set of simple behavioral schemas meant to bootstrap the learning process. In lieu of a pre-made corpus of stories, our dataset is a continuous feed of "situation samples" from a pre-trained language model, which are then parsed into FrameNet frames, mapped into simple behavioral schemas, and combined and generalized into complex, hierarchical schemas for a variety of everyday scenarios. We show that careful sampling from the language model can help emphasize stereotypical properties of situations and de-emphasize irrelevant details, and that the resulting schemas specify situations more comprehensively than those learned by other systems.
