  We present STARC (Structured Annotations for Reading Comprehension), a new
annotation framework for assessing reading comprehension with multiple choice
questions. Our framework introduces a principled structure for the answer
choices and ties them to textual span annotations. The framework is implemented
in OneStopQA, a new high-quality dataset for evaluation and analysis of reading
comprehension in English. We use this dataset to demonstrate that STARC can be
leveraged for a key new application for the development of SAT-like reading
comprehension materials: automatic annotation quality probing via span ablation
experiments. We further show that it enables in-depth analyses and comparisons
between machine and human reading comprehension behavior, including error
distributions and guessing ability. Our experiments also reveal that the
standard multiple choice dataset in NLP, RACE, is limited in its ability to
measure reading comprehension. 47% of its questions can be guessed by machines
without accessing the passage, and 18% are unanimously judged by humans as not
having a unique correct answer. OneStopQA provides an alternative test set for
reading comprehension which alleviates these shortcomings and has a
substantially higher human ceiling performance.
