  We study the current best model (KDG) for question answering on tabular data
evaluated over the WikiTableQuestions dataset. Previous ablation studies
performed against this model attributed the model's performance to certain
aspects of its architecture. In this paper, we find that the model's
performance also crucially depends on a certain pruning of the data used to
train the model. Disabling the pruning step drops the accuracy of the model
from 43.3% to 36.3%. The large impact on the performance of the KDG model
suggests that the pruning may be a useful pre-processing step in training other
semantic parsers as well.
