text: This paper collects a cloze-style fill-in-the-missing-word dataset constructed manually by English teachers to test English proficiency. Experiments are given which are claimed to show that  this dataset is difficult for machines relative to human performance. The dataset seems interesting but I find the empirical evaluations unconvincing. The models used to evaluate machine difficulty are basic language models. The problems are multiple choice with at most four choices per question. This allows multiple choice reading comprehension architectures to be used. A window of words around the blank could be used as the "question". A simple reading comprehension baseline is to encode the question (a window around the blank) and use the question vector to compute an attention over the passage. One can then compute a question-specific representation of the passage and score each candidate answer by the inner product of the question-specific sentence representation and the vector representation of the candidate answer. See "A thorough examination of the CNN/Daily Mail reading comprehension task" by Chen, Bolton and Manning.