Commonsense reasoning requires a model to make presumptions about world events via language understanding. Many methods couple pre-trained language models with knowledge graphs in order to combine the merits in language modeling and entity-based relational learning. However, although a knowledge graph contains rich structural information, it lacks the context to provide a more precise understanding of the concepts and relations. This creates a gap when fusing knowledge graphs into language modeling, especially in the scenario of insufficient paired text-knowledge data. In this paper, we propose to utilize external entity description to provide contextual information for graph entities. For the CommonsenseQA task, our model first extracts concepts from the question and choice, and then finds a related triple between these concepts. Next, it retrieves the descriptions of these concepts from Wiktionary and feed them as additional input to a pre-trained language model, together with the triple. The resulting model can attain much more effective commonsense reasoning capability, achieving state-of-the-art results in the CommonsenseQA dataset with an accuracy of 80.7% (single model) and 83.3% (ensemble model) on the official leaderboard.<\/p>\n","protected":false},"excerpt":{"rendered":"
Commonsense reasoning requires a model to make presumptions about world events via language understanding. Many methods couple pre-trained language models with knowledge graphs in order to combine the merits in language modeling and entity-based relational learning. However, although a knowledge graph contains rich structural information, it lacks the context to provide a more precise understanding 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