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Can we train a system that, on any new input, either says "don't know" ormakes a prediction that is guaranteed to be correct? We answer the question inthe affirmative provided our model family is well-specified. Specifically, weintroduce the unanimity principle: only predict when all models consistent withthe training data predict the same output. We operationalize this principle forsemantic parsing, the task of mapping utterances to logical forms. We develop asimple, efficient method that reasons over the infinite set of all consistentmodels by only checking two of the models. We prove that our method obtains100% precision even with a modest amount of training data from a possiblyadversarial distribution. Empirically, we demonstrate the effectiveness of ourapproach on the standard GeoQuery dataset.

Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings