  Existing text-to-SQL semantic parsers are typically designed for particular
settings such as handling queries that span multiple tables, domains or turns
which makes them ineffective when applied to different settings. We present
UniSAr (Unified Structure-Aware Autoregressive Language Model), which benefits
from directly using an off-the-shelf language model architecture and
demonstrates consistently high performance under different settings.
Specifically, UniSAr extends existing autoregressive language models to
incorporate three non-invasive extensions to make them structure-aware: (1)
adding structure mark to encode database schema, conversation context, and
their relationships; (2) constrained decoding to decode well structured SQL for
a given database schema; and (3) SQL completion to complete potential missing
JOIN relationships in SQL based on database schema. On seven well-known
text-to-SQL datasets covering multi-domain, multi-table and multi-turn, UniSAr
demonstrates highly comparable or better performance to the most advanced
specifically-designed text-to-SQL models. Importantly, our UniSAr is
non-invasive, such that other core model advances in text-to-SQL can also adopt
our extensions to further enhance performance.
