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

介绍一个新数据集，用于在对话上下文中重新提问问题，提供了对问题改写、网页检索和阅读理解的指标。通过组合先进的问题重写模型和开放域 QA 的竞争模型，该方法显示出了 QReCC 数据集的效力，并证明了该设置的困难性和大量改进的空间。