  Cloze tests are widely adopted in language exams to evaluate students'
language proficiency. In this paper, we propose the first large-scale
human-created cloze test dataset CLOTH, containing questions used in
middle-school and high-school language exams. With missing blanks carefully
created by teachers and candidate choices purposely designed to be nuanced,
CLOTH requires a deeper language understanding and a wider attention span than
previously automatically-generated cloze datasets. We test the performance of
dedicatedly designed baseline models including a language model trained on the
One Billion Word Corpus and show humans outperform them by a significant
margin. We investigate the source of the performance gap, trace model
deficiencies to some distinct properties of CLOTH, and identify the limited
ability of comprehending the long-term context to be the key bottleneck.
