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Recently, many datasets have been proposed to test the systematicgeneralization ability of neural networks. The companion baseline Transformers,typically trained with default hyper-parameters from standard tasks, are shownto fail dramatically. Here we demonstrate that by revisiting modelconfigurations as basic as scaling of embeddings, early stopping, relativepositional embedding, and Universal Transformer variants, we can drasticallyimprove the performance of Transformers on systematic generalization. We reportimprovements on five popular datasets: SCAN, CFQ, PCFG, COGS, and Mathematicsdataset. Our models improve accuracy from 50% to 85% on the PCFG productivitysplit, and from 35% to 81% on COGS. On SCAN, relative positional embeddinglargely mitigates the EOS decision problem (Newman et al., 2020), yielding 100%accuracy on the length split with a cutoff at 26. Importantly, performancedifferences between these models are typically invisible on the IID data split.This calls for proper generalization validation sets for developing neuralnetworks that generalize systematically. We publicly release the code toreproduce our results.

The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers