The high complexity behind SQL language and database schemas has made database querying a challenging task to human programmers. In this paper, we present our new natural language database querying (NADAQ) system as an alternative solution, by designing new translation models smoothly fusing deep learning and traditional database parsing techniques. On top of the popular encoder-decoder model for machine translation, NADAQ injects new dimensions of schema-aware bits associated with the input words into encoder phase and adds new hidden memory neurons controlled by the finite state machine for grammatical state tracking into the decoder phase. We further develop new techniques to enable the augmented neural network to reject queries irrelevant to the contents of the target database and recommend candidate queries reversely transformed into natural language. NADAQ performs well on real-world database systems over human labeled workload, returning query results at 90% accuracy.
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