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.
@article{arxiv.2306.14470,
title = {Knowledge Graph-Augmented Korean Generative Commonsense Reasoning},
author = {Dahyun Jung and Jaehyung Seo and Jaewook Lee and Chanjun Park and Heuiseok Lim},
journal= {arXiv preprint arXiv:2306.14470},
year = {2023}
}
Comments
Accepted for Data-centric Machine Learning Research (DMLR) Workshop at ICML 2023