  Seq2seq models have been shown to struggle with compositional generalisation,
i.e. generalising to new and potentially more complex structures than seen
during training. Taking inspiration from grammar-based models that excel at
compositional generalisation, we present a flexible end-to-end differentiable
neural model that composes two structural operations: a fertility step, which
we introduce in this work, and a reordering step based on previous work (Wang
et al., 2021). To ensure differentiability, we use the expected value of each
step. Our model outperforms seq2seq models by a wide margin on challenging
compositional splits of realistic semantic parsing tasks that require
generalisation to longer examples. It also compares favourably to other models
targeting compositional generalisation.
