  Generating structured query language (SQL) from natural language is an
emerging research topic. This paper presents a new learning paradigm from
indirect supervision of the answers to natural language questions, instead of
SQL queries. This paradigm facilitates the acquisition of training data due to
the abundant resources of question-answer pairs for various domains in the
Internet, and expels the difficult SQL annotation job. An end-to-end neural
model integrating with reinforcement learning is proposed to learn SQL
generation policy within the answer-driven learning paradigm. The model is
evaluated on datasets of different domains, including movie and academic
publication. Experimental results show that our model outperforms the baseline
models.
