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State-of-the-art machine learning methods exhibit limited compositionalgeneralization. At the same time, there is a lack of realistic benchmarks thatcomprehensively measure this ability, which makes it challenging to find andevaluate improvements. We introduce a novel method to systematically constructsuch benchmarks by maximizing compound divergence while guaranteeing a smallatom divergence between train and test sets, and we quantitatively compare thismethod to other approaches for creating compositional generalizationbenchmarks. We present a large and realistic natural language questionanswering dataset that is constructed according to this method, and we use itto analyze the compositional generalization ability of three machine learningarchitectures. We find that they fail to generalize compositionally and thatthere is a surprisingly strong negative correlation between compound divergenceand accuracy. We also demonstrate how our method can be used to create newcompositionality benchmarks on top of the existing SCAN dataset, which confirmsthese findings.

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data