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Compositional, structured models are appealing because they explicitlydecompose problems and provide interpretable intermediate outputs that giveconfidence that the model is not simply latching onto data artifacts. Learningthese models is challenging, however, because end-task supervision onlyprovides a weak indirect signal on what values the latent decisions shouldtake. This often results in the model failing to learn to perform theintermediate tasks correctly. In this work, we introduce a way to leveragepaired examples that provide stronger cues for learning latent decisions. Whentwo related training examples share internal substructure, we add an additionaltraining objective to encourage consistency between their latent decisions.Such an objective does not require external supervision for the values of thelatent output, or even the end task, yet provides an additional training signalto that provided by individual training examples themselves. We apply ourmethod to improve compositional question answering using neural module networkson the DROP dataset. We explore three ways to acquire paired questions in DROP:(a) discovering naturally occurring paired examples within the dataset, (b)constructing paired examples using templates, and (c) generating pairedexamples using a question generation model. We empirically demonstrate that ourproposed approach improves both in- and out-of-distribution generalization andleads to correct latent decision predictions.

Paired Examples as Indirect Supervision in Latent Decision Models