  Although neural sequence-to-sequence models have been successfully applied to
semantic parsing, they fail at compositional generalization, i.e., they are
unable to systematically generalize to unseen compositions of seen components.
Motivated by traditional semantic parsing where compositionality is explicitly
accounted for by symbolic grammars, we propose a new decoding framework that
preserves the expressivity and generality of sequence-to-sequence models while
featuring lexicon-style alignments and disentangled information processing.
Specifically, we decompose decoding into two phases where an input utterance is
first tagged with semantic symbols representing the meaning of individual
words, and then a sequence-to-sequence model is used to predict the final
meaning representation conditioning on the utterance and the predicted tag
sequence. Experimental results on three semantic parsing datasets show that the
proposed approach consistently improves compositional generalization across
model architectures, domains, and semantic formalisms.
