  Prior work in semantic parsing has shown that conventional seq2seq models
fail at compositional generalization tasks. This limitation led to a resurgence
of methods that model alignments between sentences and their corresponding
meaning representations, either implicitly through latent variables or
explicitly by taking advantage of alignment annotations. We take the second
direction and propose TPOL, a two-step approach that first translates input
sentences monotonically and then reorders them to obtain the correct output.
This is achieved with a modular framework comprising a Translator and a
Reorderer component. We test our approach on two popular semantic parsing
datasets. Our experiments show that by means of the monotonic translations,
TPOL can learn reliable lexico-logical patterns from aligned data,
significantly improving compositional generalization both over conventional
seq2seq models, as well as over other approaches that exploit gold alignments.
