  Text-to-SQL aims to map natural language questions to SQL queries. The
sketch-based method combined with execution-guided (EG) decoding strategy has
shown a strong performance on the WikiSQL benchmark. However, execution-guided
decoding relies on database execution, which significantly slows down the
inference process and is hence unsatisfactory for many real-world applications.
In this paper, we present the Schema Dependency guided multi-task Text-to-SQL
model (SDSQL) to guide the network to effectively capture the interactions
between questions and schemas. The proposed model outperforms all existing
methods in both the settings with or without EG. We show the schema dependency
learning partially cover the benefit from EG and alleviates the need for it.
SDSQL without EG significantly reduces time consumption during inference,
sacrificing only a small amount of performance and provides more flexibility
for downstream applications.
