  Table Question-Answering involves both understanding the natural language
query and grounding it in the context of the input table to extract the
relevant information. In this context, many methods have highlighted the
benefits of intermediate pre-training from SQL queries. However, while most
approaches aim at generating final answers from inputs directly, we claim that
there is better to do with SQL queries during training. By learning to imitate
a restricted portion of SQL-like algebraic operations, we show that their
execution flow provides intermediate supervision steps that allow increased
generalization and structural reasoning compared with classical approaches of
the field. Our study bridges the gap between semantic parsing and direct
answering methods and provides useful insights regarding what types of
operations should be predicted by a generative architecture or be preferably
executed by an external algorithm.
