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.

从自然语言生成结构化查询语言（SQL）是一个新兴的研究主题。本文提出了一种新的学习范式，它是对自然语言问题的答案（而非SQL查询）的间接监督。由于Internet上各个领域的问答对资源丰富，因此该范例有助于获取训练数据，并消除了困难的SQL注释工作。提出了一种与强化学习相集成的端到端神经模型，以在答案驱动的学习范式中学习SQL生成策略。该模型是在不同领域的数据集上进行评估的，包括电影和学术出版物。实验结果表明，我们的模型优于基线模型。