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.

翻译：对话式机器阅读理解任务旨在回答对话中的问题，因其广泛应用近年来成为研究热点。然而，现有CMRC基准中每段对话均分配静态篇章，这与真实场景不符，导致难以合理评估模型对真实场景的理解能力。为此，我们提出首个中文CMRC基准Orca，并进一步提供零样本/小样本设置以评估模型对多领域迁移的泛化能力。我们收集了831个热点话题驱动的对话，共包含4,742轮次。每轮对话均关联一篇与回答相关的篇章，旨在更合理地评估模型理解能力。对话话题来自社交媒体平台，覆盖33个领域，力求贴近真实场景。重要的是，Orca中的答案均为经过精心标注的自然回应，而非此前数据集中的特定片段或短短语。此外，我们实现了三种强基线方法以应对Orca的挑战。实验结果表明本CMRC基准具有重大挑战性。数据集和模型检查点可在https://github.com/nuochenpku/Orca获取。