Knowledge Graph-Augmented Korean Generative Commonsense Reasoning
Dahyun Jung Jaehyung Seo Jaewook Lee Chanjun Park Heuiseok Lim
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.