  Humans gather information by engaging in conversations involving a series of
interconnected questions and answers. For machines to assist in information
gathering, it is therefore essential to enable them to answer conversational
questions. We introduce CoQA, a novel dataset for building Conversational
Question Answering systems. Our dataset contains 127k questions with answers,
obtained from 8k conversations about text passages from seven diverse domains.
The questions are conversational, and the answers are free-form text with their
corresponding evidence highlighted in the passage. We analyze CoQA in depth and
show that conversational questions have challenging phenomena not present in
existing reading comprehension datasets, e.g., coreference and pragmatic
reasoning. We evaluate strong conversational and reading comprehension models
on CoQA. The best system obtains an F1 score of 65.4%, which is 23.4 points
behind human performance (88.8%), indicating there is ample room for
improvement. We launch CoQA as a challenge to the community at
http://stanfordnlp.github.io/coqa/
