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Humans can understand and produce new utterances effortlessly, thanks totheir compositional skills. Once a person learns the meaning of a new verb"dax," he or she can immediately understand the meaning of "dax twice" or "singand dax." In this paper, we introduce the SCAN domain, consisting of a set ofsimple compositional navigation commands paired with the corresponding actionsequences. We then test the zero-shot generalization capabilities of a varietyof recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequencemethods. We find that RNNs can make successful zero-shot generalizations whenthe differences between training and test commands are small, so that they canapply "mix-and-match" strategies to solve the task. However, whengeneralization requires systematic compositional skills (as in the "dax"example above), RNNs fail spectacularly. We conclude with a proof-of-conceptexperiment in neural machine translation, suggesting that lack of systematicitymight be partially responsible for neural networks' notorious training datathirst.

Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks