  The conversational machine reading comprehension (CMRC) task aims to answer
questions in conversations, which has been a hot research topic in recent years
because of its wide applications. However, existing CMRC benchmarks in which
each conversation is assigned a static passage are inconsistent with real
scenarios. Thus, model's comprehension ability towards real scenarios are hard
to evaluate reasonably. To this end, we propose the first Chinese CMRC
benchmark Orca and further provide zero-shot/few-shot settings to evaluate
model's generalization ability towards diverse domains. We collect 831
hot-topic driven conversations with 4,742 turns in total. Each turn of a
conversation is assigned with a response-related passage, aiming to evaluate
model's comprehension ability more reasonably. The topics of conversations are
collected from social media platform and cover 33 domains, trying to be
consistent with real scenarios. Importantly, answers in Orca are all
well-annotated natural responses rather than the specific spans or short phrase
in previous datasets. Besides, we implement three strong baselines to tackle
the challenge in Orca. The results indicate the great challenge of our CMRC
benchmark. Our datatset and checkpoints are available at
https://github.com/nuochenpku/Orca.
