Knowledge Graph-Augmented Korean Generative Commonsense Reasoning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Text GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation
Recent natural language understanding (NLU) research on the Korean language has been vigorously maturing with the advancements of pretrained language models and datasets. However, Korean pretrained language models still …
Language Model EvaluationLanguage ModelingLanguage ModellingNatural Language Understanding+2K-Act2Emo: Korean Commonsense Knowledge Graph for Indirect Emotional Expression
In many literary texts, emotions are indirectly conveyed through descriptions of actions, facial expressions, and appearances, necessitating emotion inference for narrative understanding. In this paper, we introduce K-Ac…
KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning
Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trai…
Graph AttentionText GenerationCommonsense Knowledge-Augmented Pretrained Language Models for Causal Reasoning Classification
Commonsense knowledge can be leveraged for identifying causal relations in text. In this work, we convert triples in ATOMIC2020, a wide coverage commonsense reasoning knowledge graph, to natural language text and continu…
ClassificationCommonsense Causal ReasoningLanguage ModelingLanguage ModellingExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning
Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context. Discriminative tasks are limiting because they fail to adequately evaluat…
Graph GenerationMultiple-choiceText Generation