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When answering a question, people often draw upon their rich world knowledgein addition to the particular context. Recent work has focused primarily onanswering questions given some relevant document or context, and required verylittle general background. To investigate question answering with priorknowledge, we present CommonsenseQA: a challenging new dataset for commonsensequestion answering. To capture common sense beyond associations, we extractfrom ConceptNet (Speer et al., 2017) multiple target concepts that have thesame semantic relation to a single source concept. Crowd-workers are asked toauthor multiple-choice questions that mention the source concept anddiscriminate in turn between each of the target concepts. This encouragesworkers to create questions with complex semantics that often require priorknowledge. We create 12,247 questions through this procedure and demonstratethe difficulty of our task with a large number of strong baselines. Our bestbaseline is based on BERT-large (Devlin et al., 2018) and obtains 56% accuracy,well below human performance, which is 89%.

CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge