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Knowledge Graph-Augmented Korean Generative Commonsense Reasoning

Authors :
Jung, Dahyun
Seo, Jaehyung
Lee, Jaewook
Park, Chanjun
Lim, Heuiseok
Publication Year :
2023

Abstract

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.<br />Comment: Accepted for Data-centric Machine Learning Research (DMLR) Workshop at ICML 2023

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2306.14470
Document Type :
Working Paper