Generative commonsense reasoning refers to the task of generating acceptable and logical assumptions about everyday situations based on commonsense understanding. By utilizing an existing dataset such as Korean CommonGen, language generation models can learn commonsense reasoning specific to the Korean language. However, language models often fail to consider the relationships between concepts and the deep knowledge inherent to concepts. To address these limitations, we propose a method to utilize the Korean knowledge graph data for text generation. Our experimental result shows that the proposed method can enhance the efficiency of Korean commonsense inference, thereby underlining the significance of employing supplementary data.

翻译：生成式常识推理是指基于常识理解，对日常情境生成可接受且合乎逻辑的假设的任务。通过利用诸如韩语CommonGen之类的现有数据集，语言生成模型可以学习韩语特有的常识推理能力。然而，语言模型往往未能考虑概念之间的关系以及概念固有的深层知识。为应对这些局限，我们提出一种利用韩语知识图谱数据进行文本生成的方法。实验结果表明，所提方法能提升韩语常识推理的效率，从而凸显了使用辅助数据的重要性。