  Owing to the continuous efforts by the Chinese NLP community, more and more
Chinese machine reading comprehension datasets become available. To add
diversity in this area, in this paper, we propose a new task called Sentence
Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to
fill the right candidate sentence into the passage that has several blanks. We
built a Chinese dataset called CMRC 2019 to evaluate the difficulty of the
SC-MRC task. Moreover, to add more difficulties, we also made fake candidates
that are similar to the correct ones, which requires the machine to judge their
correctness in the context. The proposed dataset contains over 100K blanks
(questions) within over 10K passages, which was originated from Chinese
narrative stories. To evaluate the dataset, we implement several baseline
systems based on the pre-trained models, and the results show that the
state-of-the-art model still underperforms human performance by a large margin.
We release the dataset and baseline system to further facilitate our community.
Resources available through https://github.com/ymcui/cmrc2019
