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We introduce a new dataset for Question Rewriting in Conversational Context(QReCC), which contains 14K conversations with 80K question-answer pairs. Thetask in QReCC is to find answers to conversational questions within acollection of 10M web pages (split into 54M passages). Answers to questions inthe same conversation may be distributed across several web pages. QReCCprovides annotations that allow us to train and evaluate individual subtasks ofquestion rewriting, passage retrieval and reading comprehension required forthe end-to-end conversational question answering (QA) task. We report theeffectiveness of a strong baseline approach that combines the state-of-the-artmodel for question rewriting, and competitive models for open-domain QA. Ourresults set the first baseline for the QReCC dataset with F1 of 19.10, comparedto the human upper bound of 75.45, indicating the difficulty of the setup and alarge room for improvement.

Open-Domain Question Answering Goes Conversational via Question Rewriting