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