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Understanding narratives requires reading between the lines, which in turn,requires interpreting the likely causes and effects of events, even when theyare not mentioned explicitly. In this paper, we introduce Cosmos QA, alarge-scale dataset of 35,600 problems that require commonsense-based readingcomprehension, formulated as multiple-choice questions. In stark contrast tomost existing reading comprehension datasets where the questions focus onfactual and literal understanding of the context paragraph, our dataset focuseson reading between the lines over a diverse collection of people's everydaynarratives, asking such questions as "what might be the possible reason of...?", or "what would have happened if ..." that require reasoning beyond theexact text spans in the context. To establish baseline performances on CosmosQA, we experiment with several state-of-the-art neural architectures forreading comprehension, and also propose a new architecture that improves overthe competitive baselines. Experimental results demonstrate a significant gapbetween machine (68.4%) and human performance (94%), pointing to avenues forfuture research on commonsense machine comprehension. Dataset, code andleaderboard is publicly available at https://wilburone.github.io/cosmos.

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning