Compositional Generalisation with Structured Reordering and Fertility Layers

• URL: http://arxiv.org/abs/2210.03183v1
• Date: Thu, 6 Oct 2022 19:51:31 GMT
• Title: Compositional Generalisation with Structured Reordering and Fertility Layers
• Authors: Matthias Lindemann, Alexander Koller, Ivan Titov
• Abstract summary: Seq2seq models have been shown to struggle with compositional generalisation. We present a flexible end-to-end differentiable neural model that composes two structural operations.
• Score: 121.37328648951993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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). 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.

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