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Sequence-to-sequence (seq2seq) models have been successful across many NLPtasks, including ones that require predicting linguistic structure. However,recent work on compositional generalization has shown that seq2seq modelsachieve very low accuracy in generalizing to linguistic structures that werenot seen in training. We present new evidence that this is a general limitationof seq2seq models that is present not just in semantic parsing, but also insyntactic parsing and in text-to-text tasks, and that this limitation can oftenbe overcome by neurosymbolic models that have linguistic knowledge built in. Wefurther report on some experiments that give initial answers on the reasons forthese limitations.

Structural generalization is hard for sequence-to-sequence models