  Building neural networks to query a knowledge base (a table) with natural
language is an emerging research topic in deep learning. An executor for table
querying typically requires multiple steps of execution because queries may
have complicated structures. In previous studies, researchers have developed
either fully distributed executors or symbolic executors for table querying. A
distributed executor can be trained in an end-to-end fashion, but is weak in
terms of execution efficiency and explicit interpretability. A symbolic
executor is efficient in execution, but is very difficult to train especially
at initial stages. In this paper, we propose to couple distributed and symbolic
execution for natural language queries, where the symbolic executor is
pretrained with the distributed executor's intermediate execution results in a
step-by-step fashion. Experiments show that our approach significantly
outperforms both distributed and symbolic executors, exhibiting high accuracy,
high learning efficiency, high execution efficiency, and high interpretability.
