based on manually engineered semantic grammars (Jia and Liang, 2016)) and learning through direct interaction with users (e.g., where a single user teaches the model new concepts (Wang et al., 2016)).
there are unique advantages to our approach, including showing (1) that non-linguists can write SQL to encode complex, compositional computations (see Fig 1 for an example), (2) that external paraphrase resources and the structure of facts from the target database itself can be used for effective data augmentation, and (3) that actual database users can effectively drive the overall learning by simply providing feedback about what the model is currently getting correct.