Generative commonsense reasoning is the capability of a language model to generate a sentence with a given concept-set that is based on commonsense knowledge. However, generative language models still struggle to provide outputs, and the training set does not contain patterns that are sufficient for generative commonsense reasoning. In this paper, we propose a data-centric method that uses automatic knowledge augmentation to extend commonsense knowledge using a machine knowledge generator. This method can generate semi-golden sentences that improve the generative commonsense reasoning of a language model without architecture modifications. Furthermore, this approach is a model-agnostic method and does not require human effort for data construction.

翻译：产生常识推理是指一种语言模型能够产生一个带有基于常识的概念集的句子的句子,但是,基因化语言模型仍然难以提供产出,而培训成套方法并不包含足以提供基因化常识推理的模式。在本文中,我们提出一种以数据为中心的方法,使用自动知识增强来利用机器知识生成器扩展常识知识。这种方法可以产生半基因句子,从而改进语言模型的基因化常识推理,而无需对结构进行修改。此外,这一方法是一种模式-不可知性方法,不需要人努力构建数据。