Knowledge Graph-Augmented Korean Generative Commonsense Reasoning

TL;DR

This paper introduces a method that leverages Korean knowledge graph data to improve generative commonsense reasoning in Korean language models, addressing limitations in concept relationship understanding.

Contribution

It proposes a novel approach to incorporate Korean knowledge graph data into language models for enhanced commonsense inference.

Findings

Improved Korean commonsense inference efficiency

Effective integration of knowledge graph data into language models

Highlighting the importance of supplementary data in reasoning tasks

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.