SCAN

Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks

This paper introduces the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences, and tests the zero-shot generalization capabilities of a variety of recurrent neural networks trained on SCAN with sequence-to-sequence methods.