  This work aims to tackle the challenging heterogeneous graph encoding problem
in the text-to-SQL task. Previous methods are typically node-centric and merely
utilize different weight matrices to parameterize edge types, which 1) ignore
the rich semantics embedded in the topological structure of edges, and 2) fail
to distinguish local and non-local relations for each node. To this end, we
propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying
relational features without constructing meta-paths. By virtue of the line
graph, messages propagate more efficiently through not only connections between
nodes, but also the topology of directed edges. Furthermore, both local and
non-local relations are integrated distinctively during the graph iteration. We
also design an auxiliary task called graph pruning to improve the
discriminative capability of the encoder. Our framework achieves
state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the
cross-domain text-to-SQL benchmark Spider at the time of writing.
