To address a mismatch between training data and development (dev) or test data: (1) perform manual error analysis to identify differences between the distributions; (2) collect or create training examples that better resemble the dev/test distribution; and (3) if appropriate, move a subset of dev/test examples into training and rebuild smaller dev and test sets, rather than moving training examples into the evaluation sets. A training-dev set is held out from training but sampled from the training distribution, allowing its performance to be compared with dev-set performance when diagnosing mismatch.