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Posing reading comprehension as a generation problem provides a great deal offlexibility, allowing for open-ended questions with few restrictions onpossible answers. However, progress is impeded by existing generation metrics,which rely on token overlap and are agnostic to the nuances of readingcomprehension. To address this, we introduce a benchmark for training andevaluating generative reading comprehension metrics: MOdeling Correctness withHuman Annotations. MOCHA contains 40K human judgement scores on model outputsfrom 6 diverse question answering datasets and an additional set of minimalpairs for evaluation. Using MOCHA, we train a Learned Evaluation metric forReading Comprehension, LERC, to mimic human judgement scores. LERC outperformsbaseline metrics by 10 to 36 absolute Pearson points on held-out annotations.When we evaluate robustness on minimal pairs, LERC achieves 80% accuracy,outperforming baselines by 14 to 26 absolute percentage points while leavingsignificant room for improvement. MOCHA presents a challenging problem fordeveloping accurate and robust generative reading comprehension metrics.

MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics