We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.IR-Net aims to address two challenges: 1) the mismatch between intents expressed in natural language (NL) and the implementation details in SQL; 2) the challenge in predicting columns caused by the large number of outof-domain words.Instead of end-to-end synthesizing a SQL query, IRNet decomposes the synthesis process into three phases.In the first phase, IRNet performs a schema linking over a question and a database schema.Then, IRNet adopts a grammar-based neural model to synthesize a SemQL query which is an intermediate representation that we design to bridge NL and SQL.Finally, IRNet deterministically infers a SQL query from the synthesized SemQL query with domain knowledge.On the challenging Text-to-SQL benchmark Spider, IRNet achieves 46.7% accuracy, obtaining 19.5% absolute improvement over previous state-of-the-art approaches.At the time of writing, IRNet achieves the first position on the Spider leaderboard. * Equal Contributions. Work done during an internship at MSRA.NL: Show the names of students who have a grade higher than 5 and have at least 2 friends. SQL: SELECT T1.name FROM friend AS T1 JOIN highschooler AS T2ON T1.student_id = T2.idWHERE T2