  Transformer based architectures are recently used for the task of answering
questions over tables. In order to improve the accuracy on this task,
specialized pre-training techniques have been developed and applied on millions
of open-domain web tables. In this paper, we propose two novel approaches
demonstrating that one can achieve superior performance on table QA task
without even using any of these specialized pre-training techniques. The first
model, called RCI interaction, leverages a transformer based architecture that
independently classifies rows and columns to identify relevant cells. While
this model yields extremely high accuracy at finding cell values on recent
benchmarks, a second model we propose, called RCI representation, provides a
significant efficiency advantage for online QA systems over tables by
materializing embeddings for existing tables. Experiments on recent benchmarks
prove that the proposed methods can effectively locate cell values on tables
(up to ~98% Hit@1 accuracy on WikiSQL lookup questions). Also, the interaction
model outperforms the state-of-the-art transformer based approaches,
pre-trained on very large table corpora (TAPAS and TaBERT), achieving ~3.4% and
~18.86% additional precision improvement on the standard WikiSQL benchmark.
