Laziness Is a Virtue When It Comes to Compositionality in Neural
  Semantic Parsing
Maxwell Crouse Pavan Kapanipathi Subhajit Chaudhury Tahira Naseem Ramon Astudillo Achille Fokoue Tim Klinger
Nearly all general-purpose neural semantic parsers generate logical forms in
a strictly top-down autoregressive fashion. Though such systems have achieved
impressive results across a variety of datasets and domains, recent works have
called into question whether they are ultimately limited in their ability to
compositionally generalize. In this work, we approach semantic parsing from,
quite literally, the opposite direction; that is, we introduce a neural
semantic parsing generation method that constructs logical forms from the
bottom up, beginning from the logical form's leaves. The system we introduce is
lazy in that it incrementally builds up a set of potential semantic parses, but
only expands and processes the most promising candidate parses at each
generation step. Such a parsimonious expansion scheme allows the system to
maintain an arbitrarily large set of parse hypotheses that are never realized
and thus incur minimal computational overhead. We evaluate our approach on
compositional generalization; specifically, on the challenging CFQ dataset and
three Text-to-SQL datasets where we show that our novel, bottom-up semantic
parsing technique outperforms general-purpose semantic parsers while also being
competitive with comparable neural parsers that have been designed for each
task.