  The dominant paradigm for semantic parsing in recent years is to formulate
parsing as a sequence-to-sequence task, generating predictions with
auto-regressive sequence decoders. In this work, we explore an alternative
paradigm. We formulate semantic parsing as a dependency parsing task, applying
graph-based decoding techniques developed for syntactic parsing. We compare
various decoding techniques given the same pre-trained Transformer encoder on
the TOP dataset, including settings where training data is limited or contains
only partially-annotated examples. We find that our graph-based approach is
competitive with sequence decoders on the standard setting, and offers
significant improvements in data efficiency and settings where
partially-annotated data is available.
