  Cloze-style reading comprehension in Chinese is still limited due to the lack
of various corpora. In this paper we propose a large-scale Chinese cloze test
dataset ChID, which studies the comprehension of idiom, a unique language
phenomenon in Chinese. In this corpus, the idioms in a passage are replaced by
blank symbols and the correct answer needs to be chosen from well-designed
candidate idioms. We carefully study how the design of candidate idioms and the
representation of idioms affect the performance of state-of-the-art models.
Results show that the machine accuracy is substantially worse than that of
human, indicating a large space for further research.
