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Despite success in many domains, neural models struggle in settings wheretrain and test examples are drawn from different distributions. In particular,in contrast to humans, conventional sequence-to-sequence (seq2seq) models failto generalize systematically, i.e., interpret sentences representing novelcombinations of concepts (e.g., text segments) seen in training. Traditionalgrammar formalisms excel in such settings by implicitly encoding alignmentsbetween input and output segments, but are hard to scale and maintain. Insteadof engineering a grammar, we directly model segment-to-segment alignments asdiscrete structured latent variables within a neural seq2seq model. Toefficiently explore the large space of alignments, we introduce a reorder-firstalign-later framework whose central component is a neural reordering moduleproducing {\it separable} permutations. We present an efficient dynamicprogramming algorithm performing exact marginal inference of separablepermutations, and, thus, enabling end-to-end differentiable training of ourmodel. The resulting seq2seq model exhibits better systematic generalizationthan standard models on synthetic problems and NLP tasks (i.e., semanticparsing and machine translation).

Structured Reordering for Modeling Latent Alignments in Sequence Transduction