  Questions asked by humans during a conversation often contain contextual
dependencies, i.e., explicit or implicit references to previous dialogue turns.
These dependencies take the form of coreferences (e.g., via pronoun use) or
ellipses, and can make the understanding difficult for automated systems. One
way to facilitate the understanding and subsequent treatments of a question is
to rewrite it into an out-of-context form, i.e., a form that can be understood
without the conversational context. We propose CoQAR, a corpus containing
$4.5$K conversations from the Conversational Question-Answering dataset CoQA,
for a total of $53$K follow-up question-answer pairs. Each original question
was manually annotated with at least 2 at most 3 out-of-context rewritings.
CoQAR can be used in the supervised learning of three tasks: question
paraphrasing, question rewriting and conversational question answering. In
order to assess the quality of CoQAR's rewritings, we conduct several
experiments consisting in training and evaluating models for these three tasks.
Our results support the idea that question rewriting can be used as a
preprocessing step for question answering models, thereby increasing their
performances.
