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We introduce SCDE, a dataset to evaluate the performance of computationalmodels through sentence prediction. SCDE is a human-created sentence clozedataset, collected from public school English examinations. Our task requires amodel to fill up multiple blanks in a passage from a shared candidate set withdistractors designed by English teachers. Experimental results demonstrate thatthis task requires the use of non-local, discourse-level context beyond theimmediate sentence neighborhood. The blanks require joint solving andsignificantly impair each other's context. Furthermore, through ablations, weshow that the distractors are of high quality and make the task morechallenging. Our experiments show that there is a significant performance gapbetween advanced models (72%) and humans (87%), encouraging future models tobridge this gap.

SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations