Fusing Context Into Knowledge Graph for Commonsense Question Answering
This work improves commonsense question answering for AI models by providing a more precise understanding of concepts, especially when labeled data is scarce.
This paper addresses the challenge of commonsense question answering by integrating external entity descriptions from Wiktionary into knowledge graphs to provide richer contextual information. This approach led to state-of-the-art performance on the CommonsenseQA dataset and the best non-generative model result on OpenBookQA.
Commonsense question answering (QA) requires a model to grasp commonsense and factual knowledge to answer questions about world events. Many prior methods couple language modeling with knowledge graphs (KG). However, although a KG contains rich structural information, it lacks the context to provide a more precise understanding of the concepts. This creates a gap when fusing knowledge graphs into language modeling, especially when there is insufficient labeled data. Thus, we propose to employ external entity descriptions to provide contextual information for knowledge understanding. We retrieve descriptions of related concepts from Wiktionary and feed them as additional input to pre-trained language models. The resulting model achieves state-of-the-art result in the CommonsenseQA dataset and the best result among non-generative models in OpenBookQA.