Commonsense question answering (QA) requires the modeling of general background knowledge about how the world operates and how entities interact with each other . Prior works leveraged manually curated commonsense knowledge graphs to help commonsense reasoningand demonstrated their effectiveness . However, these knowledge graphs are incomplete and thus may not contain the necessary knowledgefor answering the questions . In this paper, we propose to learn a multi-hop knowledge path generatorto generate structured evidence dynamically according to the questions. Our generator uses a pre-trained language modelas the backbone, leveraging a large amount of unstructured knowledge stored in the language model to supplement the incompleteness of the knowledge base. The experiments on two commonsense QA datasets demonstrate the effectiveness of our method , which improves over strong baselines significantly and also provides human interpretableexplanations for the predictions .
Commonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context . Prior works use commonsense knowledge graphs (KGs) to obtain this knowledge for reasoning . However, relying entirely on these KGs may not suffice, considering their limited coverage and the contextual dependence of their knowledge . In this paper, we augment a general commonsense QA framework with a knowledgeable path generator. By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel, multi-hop relational path. Such paths can provide structured evidence for solving commonsense questions without fine-tuning the path generator. Experiments on two datasets show the superiority of our method over previous works which fully rely on knowledge from KGs (with up to 6\% improvement in accuracy), across various amounts of training data. Further evaluation suggests that the generated paths are typically interpretable, novel, and relevant to the task .
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the modeling of general background knowledge about how the world operates and how entities interact with each other
background knowledge which is not explicitly stated in a given context
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leveraged manually curated
use
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to help commonsense reasoningand demonstrated their effectiveness
(KGs) to obtain this knowledge for reasoning
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these knowledge graphs are incomplete and thus may not contain the necessary knowledgefor answering the questions
relying entirely on these KGs may not suffice, considering their limited coverage and the contextual dependence of their knowledge
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propose to learn a
augment a general commonsense QA framework with a knowledgeable path generator. By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel,
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knowledge path generatorto generate structured evidence dynamically according to the questions. Our generator uses a pre-trained language modelas the backbone, leveraging a large amount of unstructured knowledge stored in the language model to supplement the incompleteness of the knowledge base. The experiments on two commonsense QA datasets demonstrate the effectiveness
relational path. Such paths can provide structured evidence for solving commonsense questions without fine-tuning the path generator. Experiments on two datasets show the superiority
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, which improves over strong baselines significantly and also provides human interpretableexplanations for the predictions
over previous works which fully rely on knowledge from KGs (with up to 6\% improvement in accuracy), across various amounts of training data. Further evaluation suggests that the generated paths are typically interpretable, novel, and relevant to the task