KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. The framework first grounds a question-answer pair from the semantic space to the knowledge-based symbolic space as a schema graph, a related sub-graph of external knowledge graphs. It represents schema graphs with a novel knowledge-aware graph network module named KagNet, and finally scores answers with graph representations. Our model is based on graph convolutional networks and LSTMs, with a hierarchical path-based attention mechanism. The intermediate attention scores make it transparent and interpretable, which thus produce trustworthy inferences. Using ConceptNet as the only external resource for Bert-based models, we achieved state-of-the-art performance on the CommonsenseQA, a large-scale dataset for commonsense reasoning.
Code (2)
Tasks
Common Sense ReasoningKnowledge Base Question AnsweringKnowledge GraphsNatural Language InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering
Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA). However, there is a problem …
Language ModelingLanguage ModellingQuestion AnsweringRelation Network+2Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph
Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing…
Text GenerationG-SAP: Graph-based Structure-Aware Prompt Learning over Heterogeneous Knowledge for Commonsense Reasoning
Commonsense question answering has demonstrated considerable potential across various applications like assistants and social robots. Although fully fine-tuned pre-trained Language Models(LM) have achieved remarkable per…
Graph Neural NetworkKnowledge GraphsPrompt LearningQuestion AnsweringRelation-aware Bidirectional Path Reasoning for Commonsense Question Answering
Commonsense Question Answering is an important natural language processing (NLP) task that aims to predict the correct answer to a question through commonsense reasoning. Previous studies utilize pre-trained models on la…
Graph Neural NetworkKnowledge GraphsQuestion AnsweringRelationMICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation
Commonsense reasoning tasks such as commonsense knowledge graph completion and commonsense question answering require powerful representation learning. In this paper, we propose to learn commonsense knowledge representat…
Contrastive LearningKnowledge Graph CompletionKnowledge GraphsQuestion Answering+2