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When training and evaluating machine reading comprehension models, it is veryimportant to work with high-quality datasets that are also representative ofreal-world reading comprehension tasks. This requirement includes, forinstance, having questions that are based on texts of different genres andrequire generating inferences or reflecting on the reading material.  In this article we turn our attention to RACE, a dataset of English texts andcorresponding multiple-choice questions (MCQs). Each MCQ consists of a questionand four alternatives (of which one is the correct answer). RACE wasconstructed by Chinese teachers of English for human reading comprehension andis widely used as training material for machine reading comprehension models.By construction, RACE should satisfy the aforementioned quality requirementsand the purpose of this article is to check whether they are indeed satisfied.  We provide a detailed analysis of the test set of RACE for high-schoolstudents (1045 texts and 3498 corresponding MCQs) including (1) an evaluationof the difficulty of each MCQ and (2) annotations for the relevant pieces ofthe texts (called "bases") that are used to justify the plausibility of eachalternative. A considerable number of MCQs appear not to fulfill basicrequirements for this type of reading comprehension tasks, so we additionallyidentify the high-quality subset of the evaluated RACE corpus. We alsodemonstrate that the distribution of the positions of the bases for thealternatives is biased towards certain parts of texts, which is not necessarilydesirable when evaluating MCQ answering and generation models.

EMBRACE: Evaluation and Modifications for Boosting RACE