Cloze tests play an essential role in language assessment and help language learners improve their skills.In this paper, we propose a novel task called Cloze Quality Estimation (CQE) — a zero-shot task of evaluating whether a cloze test is of sufficient “high-quality” for language assessment based on two important factors: reliability and validity.We have taken the first step by creating a new dataset named CELA for the CQE task, which includes English cloze tests and corresponding evaluations about their quality annotated by native English speakers, which includes 2,597 and 1,730 instances in aspects of reliability and validity, respectively.We have tested baseline evaluation methods on the dataset, showing that our method could contribute to the CQE task, but the task is still challenging.

New things are being created and new words are constantly being added to languages worldwide. However, it is not practical to translate them all manually into a new foreign language. When translating from an alphabetic language such as English to Chinese, appropriate Chinese characters must be assigned, which is particularly costly compared to other language pairs. Therefore, we propose a task of generating and evaluating new translations from English to Chinese focusing on named entities. We defined three criteria for human evaluation—fluency, adequacy of pronunciation, and adequacy of meaning—and constructed evaluation data based on these definitions. In addition, we built a baseline system and analyzed the output of the system.