Schema-Aware Multi-Task Learning for Complex Text-to-SQL
Yangjun Wu Han Wang
Conventional text-to-SQL parsers are not good at synthesizing complex SQL
queries that involve multiple tables or columns, due to the challenges inherent
in identifying the correct schema items and performing accurate alignment
between question and schema items. To address the above issue, we present a
schema-aware multi-task learning framework (named MTSQL) for complicated SQL
queries. Specifically, we design a schema linking discriminator module to
distinguish the valid question-schema linkings, which explicitly instructs the
encoder by distinctive linking relations to enhance the alignment quality. On
the decoder side, we define 6-type relationships to describe the connections
between tables and columns (e.g., WHERE_TC), and introduce an operator-centric
triple extractor to recognize those associated schema items with the predefined
relationship. Also, we establish a rule set of grammar constraints via the
predicted triples to filter the proper SQL operators and schema items during
the SQL generation. On Spider, a cross-domain challenging text-to-SQL
benchmark, experimental results indicate that MTSQL is more effective than
baselines, especially in extremely hard scenarios. Moreover, further analyses
verify that our approach leads to promising improvements for complicated SQL
queries.